• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.815-821
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• 2009

Information processing in variable and noisy environments is usually accomplished by means of adaptive filters. Among various adaptive algorithms, Least Mean Square (LMS) has become the most popular for its robustness, good tracking capabilities and simplicity, both in terms of computational load and easiness of implementation. In practical application of the LMS algorithm, the most important key parameter is the Step Size. As is well known, if the Step Size is large, the convergence rate of the algorithm will be rapid, but the steady state mean square error (MSE) will increase. On the other hand, if the Step Size is small, the steady state MSE will be small, but the convergence rate will be slow. Many researches have been proposed to alleviate this drawback by using a variable Step Size. In this paper, a new variable Step Size LMS(VSSLMS) called Categorized VSSLMS (CVSSLMS) is proposed. CVSSLMS updates the Step Size by categorizing the current status of the gradient, hence significantly improves the convergence rate. The performance of the proposed algorithm was verified from the view point of convergence rate, Excessive Mean Square Error(EMSE), and complexity through experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11C
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• pp.948-956
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• 2010

Griffiths and Jim has proposed a beamforming structure called GSC algorithm, in which antenna elements are grouped into main-channel and sub-channel, and sidelobe is reduced by applying adaptive LMS algorithm. This paper proposes WLMS-GSC algorithm where the Haar and Daubechies wavelet filters are used to process array antenna output, instead of using subtractor filter. We analyze characteristics of the proposed WLMS-GSC algorithm. The WLMS-GSC has characteristic of reducing the computational requirement one-half compared to the LMS-GSC algorithm. In addition, we obtain MSE characteristics and adaptive beampattern of WLMS-GSC algorithm, and compared with the performance of LMS-GSC algorithm. The simulation results show that the WLMS-GSC algorithm proposed in this paper gives better or almost the same performance, compared to the LMS-GSC algorithm. In addition, the newly proposed structure has advantage of low computational requirements.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.179-182
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• 2004

무선통신분야에서 LMS5(Least Mean Square) 알고리즘은 식이 간단하고 계산량이 비교적 적기 때문에 널리 사용되고 있다. 그러나 시간영역에서 처리할 경우 입력신호의 고유치 변동폭이 넓게 분포되어 수렴속도가 저하하는 문제점이 있다. 이를 해결하기 위하여 신호를 FFT(Fast Fourier Trasnform)나 DCT(Discrete Cosine Transform)로 변환하여 신호간의 상관도를 제거함으로써 시간영역에서 LMS알고리즘을 적용할 때 보다 수렴속도를 크게 향강시킬 수 있다. 본 논문에서는 수렴속도 향상을 위해 시간영역의 적응 알고리즘을 직교변환인 고속웨이브렛(wavelet)변환을 이용하여 변환영역에서 수행하며, 짧은 필터계수를 가지는 DWT(Discrete Wavelet Transform)특성에 맞는 Fast running FIR 알고리즘을 이용하여 WTLMS(Wavelet Transform LMS)적응알고리즘을 통신시스템에 적용한다. 적응 알고리즘의 성능향상을 위하여 시간에 따라 적응상수의 크기를 가변시켜 수렴 초기에는 큰 적응상수로 따른 수렴이 가능하도록 하고 점차 적응상수의 크기를 줄여서 misadjustment도 줄이는 방법의 적응 알고리즘을 제안하였다. 제안한 알고리즘을 실제로 적응잡음제거기(adaptive noise canceler)에 적용하여 컴퓨터 시뮬레이션을 하였으며, 각 알고리즘들의 계산량, 수렴속도를 이용하여 각각 비교, 분서하여 그 성능이 우수함을 입증하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.3
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• pp.158-163
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• 2009

LMS(Least Mean Square) algorithm requires simple equation and is used widely because of the low complexity. If the convergence speed increase, LMS algorithm has a divergence in case of sharp environment changes. And if a stability increase, the convergence speed becomes slow. This algorithm based on a trade off between fast convergence and system stability. To improve this problem, VSSLMS (Variable Step Size LMS) algorithm was developed. The VSSLMS algorithm improved the convergence speed and performance as adjusting step size using error signal. In this paper, I-VSSLMS algorithm is proposed tor improve the performance of adaptive noise canceller in real-time environments. The proposed algorithm is applied to adaptive noise canceller using TMS320C6713 DSP board and we did simulation by real time. Then we compared performance of each algorithm and demonstrated that proposed algorithm has superior performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.4 no.3
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• pp.224-229
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• 2009

In this paper, an efficient signal interference control technique to improve the convergence speed of Adaptive transversal filter with LMS algorithm is introduced. The convergence characteristics of the proposed algorithm, whose coefficients are multiply adapted in a symbol time period by recycling the received data, are analyzed to prove theoretically the improvement of convergence speed. According as the step-size parameter ${\mu}$ is increased, the rate of convergence of the algorithm is controlled. Increasing the eigenvalue spread has the effect of controlling down the rate of convergence of the adaptive equalizer and also increasing the steady-state value of the average squared error and also demonstrate the superiority of signal interference control to the filter algorithm increasing convergence speed by (B+1) times due to the data-recycling LMS Algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.9
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• pp.764-774
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• 1990

In this paper, make a study on comparison of adaptive filters for predictor characteristics that transversal, lattice, and joint process lattice filter is using the LMS algorithm that is simple structure and pracotical application is easy. The theoical background and structure of each adaptive filters exhibit for practical design. Adaptive convergence condition for optimal weight vector and optimal reflection coefficient make clear, and it is also shown through computer simulation. The error signals and noise characteristics of these filters make a comparative study. In view of the results, joint process lattice filter is shown that most superior characteristic in these adaptie filters.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.508-511
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• 2003

For the purpose of equalisation of rapidly time variant channels, We derive a novel adaptive algorithm the amplitude Banded LMS(ABLMS), which implements a non-linear adaptation based on a coefficient matrix. Then we develop the ABLMS algorithm as the adaptation procedure for a linear transversal equalise.(LTE) and a decision feedback equaliser(DFE) where a parallel adaption scheme is deployed.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.2
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• pp.95-98
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• 2013

The LMS(Least Mean Square) algorithm, one of the most famous, is generally used because of tenacity and high mating spots and simplicity of realization, But it has trade-off between nonuniform collection and EMSE(Excess mean square error). To overcome this weakness, a variable step size is used widely, but it needs a lot of calculation loads. In this paper, we suggest changed algorithm in case of environment changes of cars and reduce amount of calculation as it uses original signal and noise signal of IGC(Instantaneous Gain Control) algorithm. In this paper, logarithmic function is removed because of real-time processing IGC. The performance of proposed algorithm is tested to adaptive noise canceller in automobile.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.3
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• pp.79-88
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• 1999

One of the most popular algorithm in adaptive signal processing is the least mean square(LMS) algorithm. The majority of these papers examine the LMS algorithm with a constant step size. The choice of the step size reflects a tradeoff between misadjustment and the speed of adaptation. Subsequent works have discussed the issue of optimization of the step size or methods of varying the step size to improve performance. However there is as yet no detailed analysis of a variable step size algorithm that is capable of giving both the speed of adaptation and convergence. In this paper we propose a new variable step size algorithm where the step size adjustment is controlled by square of the prediction error. The simulation results obtained using the new algorithm about noise canceller system and system identification are described. They are compared to the results obtained for other variable step size algorithm. function.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1329-1338
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• 1999

In this paper, an efficient signal interference control technique to improve the convergence speed of LMS algorithm is introduced. The convergence characteristics of the proposed algorithm, whose coefficients are multiply adapted in a symbol time period by recycling the received data, are analyzed to prove theoretically the improvement of convergence speed. According as the step-size parameter $\mu$ is increased, the rate of convergence of the algorithm is controlled. Also, a increase in the step-size parameter $\mu$ has the effect of reducing the variation in the experimentally computed learning curve. Increasing the eigenvalue spread has the effect of controlling down the rate of convergence of the adaptive equalizer and also increasing the steady-state value of the mean squared error and also demonstrate the superiority of signal interference control to the filter algorithm increasing convergence speed by (B+1) times due to the data-recycling LMS technique.