• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.50-52
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• 2010

통신 시스템의 성능을 향상시키는 핵심 문제 중에 하나인 채널을 추정하는 문제는 다양한 분야에서 연구되고 있다. 채널의 sparse한 특징으로 인해 기존의 linear square나 minimum mean square error보다 발전된 $l_1$-norm minimization 방법 등이 많이 연구되고 있다. 이에 본 논문은 sparse한 채널의 특징과 천천히 변화하는 채널환경 특징을 이용하여 기존의 방법에 비해 더 높은 성능의 채널 추정 기법을 연구한다. 천천히 변화하는 채널환경의 특징으로 인해 이전 채널 정보를 현재 채널 추정에 사용할 수 있고 sparse한 채널의 특징으로 $l_1$-norm minimization을 사용할 수 있다. 이러한 두 가지의 정보를 이용하여 weighted $l_1$-norm minimization 이용한 support detection후 MMSE를 이용한 채널 추정기법을 연구한다.

• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.53-58
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• 2012

In this paper, we evaluate and analyze representative estimation methods for the sparse channel. In order to evaluate error performance of matching pursuit(MP) and minimum mean square error(MMSE) algorithm, lower bound of MMSE is determined by Cramer-Rao bound and compared with upper bound of MP. Based on analysis of those bounds, mean square error of MP which is effective in the estimation of sparse channel can be larger than that of MMSE according to the number of estimated tap and signal-to-noise ratio. Simulation results show that the performances of both algorithm are reversed on the sparse channel with Rayleigh fading according to signal-to-noise ratio.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.5
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• pp.59-65
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• 2012

In this paper, we investigate the compressed sensing (CS) algorithms for modeling a super-resolution near-field structure (super-RENS) disc system with a sparse Volterra filter. It is well known that the super-RENS disc system has severe nonlinear inter-symbol interference (ISI). A nonlinear system with memory can be well described with the Volterra series. Furthermore, CS can restore sparse or compressed signals from measurements. For these reasons, we employ the CS algorithms to estimate a sparse super-RENS read-out channel. The evaluation results show that the CS algorithms can efficiently construct a sparse Volterra model for the super-RENS read-out channel.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1061-1066
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• 2014

Transmission characteristics of the sound propagation is very complicate and sparse in shallow water. To increase the performance of underwater acoustic communication system, lots of channel estimation technique has been proposed. In this paper, we proposed the channel estimation based on LMS(Least Mean Square) algorithm which has faster convergence speed than conventional sparse-aware LMS algorithms. The proposed method combines $L_p$-norm LMS with soft decision process. Simulation was performed by using the sound velocity profile which acquired in real sea trial. As a result, we confirmed that the proposed method shows the improved performance and faster convergence speed than conventional methods.

• Journal of IKEEE
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• v.16 no.3
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• pp.258-264
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• 2012

Although linear channel estimation for the frequency selective fading channel has been widely deployed, its accuracy depends on the number of pilots to probe the channel. Thus, it is unavoidable to employ large number of pilots to enhance the channel estimation performance, which essentially leads to the degradation of the transmission efficiency. It even does not utilize the sparseness of the multipath channel. In this paper a sparse channel estimation scheme based on the matching pursuit algorithm and a pilot assignment method, which minimizes the coherence, are proposed. The simulation results reveal that the proposed algorithm shows superior channel estimation performance with fewer pilots to the LS based ones.

• The Journal of the Acoustical Society of Korea
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• v.35 no.5
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• pp.383-388
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• 2016

We develop two methods to select a constant in the RLS (Recursive Least Squares) with the convex regularization. The RLS with the convex regularization was proposed by Eksioglu and Tanc in order to estimate the sparse acoustic channel. However the algorithm uses the regularization constant which needs the information about the true channel response for the best performance. In this paper, we propose two methods to select the regularization constant which don't need the information about the true channel response. We show that the estimation performance using the proposed methods is comparable with the Eksioglu and Tanc's algorithm.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.32-37
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• 2020

This paper proposes a new l₁-norm-Recursive Least Squares (RLS) algorithm which is numerically more robust than the conventional l₁-norm-RLS. The l₁-norm-RLS was proposed by Eksioglu and Tanc in order to estimate the sparse acoustic channel. However the algorithm has numerical instability in the inverse matrix calculation. In this paper, we propose a new algorithm which is robust against the numerical instability. We show that the proposed method improves stability under several numerically erroneous situations.

• Journal of Broadcast Engineering
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• v.10 no.1 s.26
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• pp.4-13
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• 2005

An equalization algorithm is proposed to guarantee a stable performance in fast fading channels for digital television (DTV) systems from the advanced television system committee (ATSC) standard. In channels with high Doppler shifts, the conventional equalization algorithm shows severe performance degradation. Although the conventional equalizer compensates poor channel conditions to some degree, long filter taps required to overcome long delay profiles are not suitable for fast fading channels. The Proposed sparse equalization algorithm is robust to the multipaths with long delay Profiles as well as fast fading by utilizing channel estimation and equalizer initialization. It can compensate fast fading channels with high Doppler shifts using a filter tap selection technique as well as variable step-sizes. Under the ATSC test channels, the proposed algorithm is analyzed and compared with the conventional equalizer. Although the proposed algorithm uses small number of filter taps compared to the conventional equalizer, it is stable and has the advantages of fast convergence and channel tracking.

• Journal of Broadcast Engineering
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• v.10 no.2
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• pp.166-173
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• 2005

In this paper, we propose a mobile channel estimation algorithm using matching pursuit algorithm for orthogonal frequency division multiplexing (OFDM) systems. Least square (LS) algorithm, which is used as a conventional channel estimation algorithm for OFDM systems, has error probability of channel estimation affected by effects of noise. By estimating the channel of sparse type, the proposed algorithm reduces effects of noise during time intervals that multi-path signal doesn't exist. The proposed algorithm estimates a mobile receivingchannel using pilot information transmitted consequently. We compare performance of the proposed algorithm with the LS algorithm by measuring symbol error rate with 64QAM under a mobile multi-path fading channel model.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.138-144
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• 2012

In this paper, we introduce the time-domain equalizer in orthogonal frequency division multiplexing (OFDM) systems using orthogonal matching pursuit (OMP) algorithm. Since OFDM system inserts guard intervals, it shows robust performance against multi-path fading. However, in Doppler channel, inter-carrier interference (ICI) occurs because an orthogonality of sub-carriers does not maintain. A least squares (LS) algorithm is common method of time-domain equalizer, but if a channel length is longer, the performance deteriorates by noise. The multi-path fading is a summation of the different delay signal. And that has sparse properties in time-domain. Because the OMP algorithm of the compressive sensing (CS) algorithm restores the channel by choosing the important elements of sparse channel, it can reduce the influence of noise. We simulate the performance of time-domain equalizer in OFDM system with various channel environments using OMP algorithm compared with other equalization method.