• Journal of Satellite, Information and Communications
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• v.10 no.2
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• pp.19-23
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• 2015

In this paper, we present the return link ACM method to improve the link availability and system throughput for satellite communication service. Also, we describe the optimization of an algorithm for channel prediction using the LMS (Least Mean Square) adaptive filter and the MODCOD (Modulation & Code rate) decision. The simulation results show that the optimized filter taps and step-size of adaptive filter are 2 and 0.00026, respectively. And also confirms the required SNR margin for minimization of MODCOD decision error is 0.3dB.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.601-610
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• 2004

In this paper, the performance of an adaptive array antenna using Constant Modulus Algorithm (CMA) based on the signal structure for the IMT-2000 3GPP specification reverse link of an asynchronous direct sequence code division multiple access (DS-CDMA) system are evaluated. In addition, the performance is compared with the array antenna using Least Mean Square (LMS) based on the training signal. The simulation parameters such as the number of multipath, mu10pa1h intensity profiles between path, spreading gain and multiuser etc., are considered in the Monte Carlo simulation. Simulation results demonstrate an adaptive array antenna using CMA may give more capacity gain than the amy antenna using LMS in the case of multipath fading channel.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1095-1106
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• 1994

This paper presents a method for determining optimal grayscale function processing(FP) morphological filters under the least square (LMS) error criterion. The optimal erosion and dilation filters with a grayscale structuring element(GSE) are determined by minimizing the mean square error (MSE) between the desired signal and the filter output. It is shown that convergence of the erosion and dilation filters can be achieved by a proper choice of the step size parameter of the LMS algorithm. In an attempt to determine optimal closing and opening filters, a matrix representation of both opening and closing with a basis matrix is proposed. With this representation, opening and closing are accomplished by a local matrix operation rather than cascade operations. The LMS and back-propagation algorithm are utilzed for obtaining the optimal basis matrix for closing and opening. Some results of optimal morphological filters applied to 2-D images are presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2C
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• pp.272-282
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• 2004

This paper introduces a regularized mixed norm multi-channel image restoration algorithm using both within-and between- channel deterministic information. For each channel a functional which combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed. We introduce a mixed norm parameter that controls the relative contribution between the LMS and the LMF, and a regularization parameter defining the degree of smoothness of the solution, where both parameters are updated at each iteration according to the noise characteristics of each channel. The novelty of the proposed algorithm is that no knowledge of the noise distribution for each channel is required and that the parameters mentioned above are adjusted based on the partially restored image.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.315-318
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• 2001

We propose two new blind LMS and MMSE algorithms called projection-based least mean square (PB-LMS) and projection-based minimum mean square error (PB-MMSE) for smart antennas. Both algorithms employ the finite constellation property of digital signal to transform the conventional LMS and MMSE algorithms into blind algorithms. Computer simulations were carried out in the AWGN channel and Rayleigh fading channel with AWGN in CDMA environment to verify the performance of the two proposed algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4C
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• pp.305-311
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• 2008

In this paper, we proposed an algorithm for adaptive noise cancellation (ANC) using the variable step size normalized least mean square (VSSNLMS) in real-time automobile environment. As a basic algorithm for ANC, the LMS algorithm has been used for its simplicity. However, the LMS algorithm has problems of both convergence speed and estimation accuracy in real-time environment. In order to solve these problems, the VSSLMS algorithm for ANC is considered in nonstationary environment. By computer simulation using real-time data acquisition system(USB 6009), VSSNLMS algorithm turns out to be more effective than the LMS algorithm in both convergence speed and estimation accuracy.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.242-246
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• 2003

본 논문에서는 친사용자 환경의 시뮬레이션 환경을 제공하는 SPW 시뮬레이션 플랫포음을 이용하여 스마트 안테나 CDMA 이동통신 시스템을 구현하여 빔형성에 있어서 고정(non-adaptive)의 경우와 적응(adaptive)의 경우로 나누어 성능 분석을 수행하였다. 특히 적응인 경우는 LS(Least Square) 와 LMS (Least Mean Square) 의 적응 알고리즘을 비교하였으며 간섭 기지국의 각도 (Interferer signal angle) 변화에 따른 성능도 비교하였다. SPW 시뮬레이션 결과, 고정 경우보다는 적응 경우가 3［㏈］이상의 이득을 얻을 수 있었으며 LMS 보다는 LS 의 성능이 우수함을 알 수 있었다.

• Journal of KSNVE
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• v.4 no.1
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• pp.59-69
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• 1994

A new LMS algorithm titled constrained LMS' is proposed for problems with constrained structure. The conventional LMS algorithm can not be used because it destroys the constrained structures of the weights or parameters. Proposed method uses error-back propagation, which is popular in training neural networks, for error minimization. The illustrative examplesare shown to demonstrate the applicability of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.95-101
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• 2009

Among the adaptive noise cancellers (ANC), the least mean square (LMS) algorithm has probably become the most popular algorithm because of its robustness, good tracking properties, and simplicity of implementation. However, it has non-uniform convergence and a trade-off between the rate of convergence and excess mean square error (EMSE). To overcome these shortcomings, a number of variable step size least mean square (VSSLMS) algorithms have been researched for years. These LMS algorithms use a complex variable step method approach for rapid convergence but need high computational complexity. A variable step approach can impair the simplicity and robustness of the LMS algorithm. The proposed instantaneous gain control (IGC) algorithm uses the instantaneous gain value of the original signal and the noise signal. As a result, the IGC algorithm can reduce computational complexity and maintain better performance.