• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.996-1007
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• 1996

In this paper, a blind adaptation method for a decision feedback equalizer (DFE) is proposed to deal with nominimum phase channels. This equalizer is composed of a linear transversal filter and a prediction error filter which are trained separately using constant modulus and decision feedback prediction algorithms, respectively, during the learnign time. The proposed algorithm guaranetees the DFE to converge to a suboptimal point on the condition that a linear transversal of the proposed scheme is illustrated and the performance is compared with conventional blind equlization 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.9 no.4
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• pp.11-22
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• 1999

Least-mean-square(LMS) adaptive filters have proven to be extremely useful in a number of signal processing tasks. However LMS adaptive filter suffer from a slow rate of convergence for a given steady-state mean square error as compared to the behavior of recursive least squares adaptive filter. In this paper an efficient signal interference control technique is introduced to improve the convergence speed of LMS algorithm with tap weighted vectors updating which were controled by reusing data which was abandoned data in the Adaptive transversal filter in the scheme with data recycling buffers. The computer simulation show that the character of convergence and the value of MSE of proposed algorithm are faster and lower than the existing LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and increase speed of convergence in avoidance burden of computational complexity in reality when it was experimented having the same condition of LMS algorithm.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.107-110
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• 2003

본 논문은 LTJ 적응필터의 실용적 구현에 관한 연구이다. 음성코덱(codec)을 사용하는 응용분야에서는 코덱 복호화단의 LPC 계수정보를 얻을 수 있으므로 이를 반사계수로 변환하여 사용하므로서 반사계수 적응에 소용되는 계산량을 감소시킬 수 있으며, 코덱에서는 프레임 또는 서브프레임 단위로 LPC 계수를 적응시키므로 시변 변환 영역 적응필터에 해당하는 LTJ 적응필터의 필터 계수 보상에 필요한 계산량을 감소시킬 수 있다. 실제 음성신호를 사용하여 제안된 실용적 구현 방법의 타당성을 검증하였다.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.28-31
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• 1991

In this paper, it is proposed to improve the robustness of the Fast Recursive Least Squarea(FRLS) algolithms with the exponential weighting, which is an important class of algolithms for adaptive filtering. It is well known that the FRLSalgolithm is numerically unstable with exponential weighting factor λ<1. However, introducing some gains into this algolithms, numerical errors can be reduced. An accurately choice of thegains then leads to a numerically stable FRLS algolithm with a complexity of 8m mulitiplications and we shown it by computer simulations.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.95-102
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• 1996

This paper describes the realization of a rela-time adaptive acoustic echo canceller, which adopts a microprogramming method, for removing acoustical echoes in speakerphone systems using th eADSP-2101 microprocessor with a pipeline and modified harvard architecture. We apply the LMS (least mean square) algorithm to estimate the coefficients of a transversal FIR filter. For the acustic adaptive echo canceller, we propose a parallel operation programming to imrove algorithm execution speed and apply a nonlinear quantization to reduce the quantization error caused by large dynamic range of voice signal.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.8
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• pp.914-921
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• 1992

In this paper, we introduce a Walsh network and an LMS algorithm, and show how these can be realized as an adaptive equalizer. The Walsh network is built from a set of Walsh and Block pulse functions. In the LMS algorithm, the convergence factor is an important design parameter because it governs stability and convergence speed, which depend on the proper choice of the convergence facotr. The conventional adaptation techniques use a fixed time constant convergence factor by the method of trial and error. In this paper, we propose an optimal method in the choice of the convergence factor. The proposed algorithm depends on the received signal and the output of the Walsh network in real time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5B
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• pp.830-841
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• 2000

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-1, we may compute the updated estimate of this vector at iteration a upon the arrival of new data. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. In this paper, we propose new tap weight updated RLS algorithm in adaptive transversal filter with data-recycling buffer structure. We prove that convergence speed of learning curve of RLS algorithm with data-recycling buffer is faster than it of exiting RL algorithm to mean square error versus iteration number. Also the resulting rate of convergence is typically an order of magnitude faster than the simple LMS algorithm. We show that the number of desired sample is portion to increase to converge the specified value from the three dimension simulation result of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of convergence character in performance, is achieved at the (B+1)times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.691-698
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• 2003

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-l, we may compute the updated estimate of this vector at iteration n upon the arrival of new data. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. In this paper, we propose new tap weight updated RLS algorithm in adaptive transversal filter with data-recycling buffer structure. We prove that convergence speed of learning curve of RLS algorithm with data-recycling buffer is faster than it of exiting RLS algorithm to mean square error versus iteration number. Also the resulting rate of convergence is typically an order of magnitude faster than the simple LMS algorithm. We show that the number of desired sample is portion to increase to converge the specified value from the three dimension simulation result of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of convergence character in performance, is achieved at the B times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1475-1478
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• 2002

This paper presents a performance analysis of an improved adaptive algorithm proposed by the authors recently. It is based on the normalized least mean square (NLMS) algorithm, which Is one of the major techniques to adapt the cofficients of a transversal filter. Generally, the performance of an adaptive algorithm is often discussed by investigating the mis-adjustment. In this paper, unlike these approaches, a novel analytical method is considered. letting the parameters so that the residual mean square error (MSE) after the convergence of the algorithm is equal to that of the NLMS algorithm, the MSE level is compared. It is shown that the theoretical analysis is agreed with the simulation results.