• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.13 no.2
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• pp.130-135
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• 2002

A new Sign algorithm which has improved convergence speed is presented. The data-recycling technique, whose coefficients are multiply adapted in a symbol time period by recycling the received data, is applied to Sign algorithm which has few multiplications. Sign algorithm has very few multiplications and is the most easily implemented, but it gives small rate of convergence relative to others. The proposed algorithm combines the advatage of Sign algorithm, few multiplications, and the virtue of Data-Recycling LMS algorithm, simplicity and fast convergence. The results of computer simulation show that the proposed algorithm has 2 times faster convergence rate than that of LMS algorithm. Comparing to Data-Recycling LMS algorithm, in similar convergence conditions, it requires half fewer multiplications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.1905-1913
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• 1996

The convergence characteristics of an LMS type Equalizer whose coefficient are multiple adapted in a symbol time period by recycling the received data are analyzed. The theoretical analysis shows that the data-recycling LMS technique can increase convergence speed by (B+1) times, where B is the number of recycled data. The results of the computer simulation demonstrate that the simulation results are in accordence with the theoretical analysis and the superiority of the equalizer algorithm.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10a
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• pp.624-626
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• 1998

In this paper. a new simple and efficient 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 analysis to prove theoretically the improvement of convergence speed. The theoretical analysis shows that the data-recycling LMS technique can increase convergence speed by (B+1) times, where B is the number of recycled data. The results of the computer simulation demonstrate that the simulation results are in accordance with the theoretical analysis and the superiority of the filter algorithm

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.22-33
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• 2005

In this paper, an efficient signal interference control technique to improve the high convergence speed of LMS algorithms is introduced in the adaptive transversal filter of DS/SS. The convergence characteristics of the proposed algorithm, whose coefficients are multiply adapted in a symbol time period by recycling the received data, is analyzed to prove theoretically the improvement of high convergence speed. According as the step-size parameter ${\mu}$ is increased, the rate of convergence of the algorithm is controlled. Also, an increase in the stop-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 which is downed the rate of convergence of the adaptive equalizer. Increasing the steady-state value of the average squared error, proposed algorithm 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.2
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• pp.88-94
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• 2013

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 thestep-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 technique.

• Journal of Electrical Engineering and information Science
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• v.3 no.1
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• pp.126-130
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• 1998

In this paper, a new interference rejection filter using the data-recycling technique is presented. The reference signal of the interference rejection filter is formed by using chip decisions, which is correlated with the narrowband interference components of the received signal. The decision feedback techniques reduce the distortion of the desired signal, which is introduced by the interference rejection filter through he use of feedback chip decisions. In order to update the filter coefficients a simple and efficient data-recycling technique that has improved performance over the conventional LMS algorithm is used. The performance of the proposed interference rejection filter is compared to filter with conventional LMS linear filter. The results show that the convergence speed of the proposed interference rejection filter using decision feedback and data-recycling technique is significantly faster than that of the conventional filters. Also, BER performance of the interference rejection filter demonstrates the superiority of the proposed filter algorithm.

• 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 Korean Institute of Information and Commucation Sciences Conference
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• 1998.11a
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• pp.574-577
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• 1998

In this paper, a new simple and efficient technique to improve the convergence speed of LMS algorithm is proposed in an interference-limited multi-path fading environment as encountered in indoor wireless communications. 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. The theoretical analysis shows that the data-recycling in technique can increase convergence speed by (B+1) times without increasing the computational complexity substantially where B is the number of recycled data. The results of the computer simulation demonstrate that the simulation results are in accordance with the theoretical analysis and the superiority of the filter algorithm.