• Journal of Communications and Networks
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• v.6 no.1
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• pp.19-25
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• 2004

In this paper, we present a trellis-based blind channel estimation and equalization technique coupling two kinds of adaptive Viterbi algorithms. First, the initial blind channel estimation is accomplished by incorporating the list parallel Viterbi algorithm with the least mean square (LMS) updating approach. In this operation, multiple trellis mappings are preserved simultaneously and ranked in terms of path metrics. Equivalently, multiple channel estimates are maintained and updated once a single symbol is received. Second, the best channel estimate from the above operation will be adopted to set up the whole trellis. The conventional adaptive Viterbi algorithm is then applied to detect the signal and further update the channel estimate alternately. A small delay is introduced for the symbol detection and the decision feedback to smooth the noise impact. An automatic switch between the above two operations is also proposed by exploiting the evolution of path metrics and the linear constraint inherent in the trellis mapping. Simulation has shown an overall excellent performance of the proposed scheme in terms of mean square error (MSE) for channel estimation, robustness to the initial channel guess, computational complexity, and channel equalization.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.343-348
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• 2011

This paper introduces a design for real time wireless headset using ANC (active noise control) system based on NFxLMS adaptive filter algorithm. The training time of the proposed system is significantly reduced by using the RMS delay spread of a channel as an error correction parameter, and convergence rate of the FxLMS filter has been improved with updating the coefficients of the NFxLMS filter, which we have got during the training process. Our system has shorter training time and better convergence rate at the same noise reduction level than the conventional system under real noisy environment.

• Journal of IKEEE
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• v.6 no.2 s.11
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• pp.111-118
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• 2002

In this paper, we present a variable block method to reduce additive computational requirements in determining step-size of variable step-size LMS (VS-LMS) algorithms. The block length is inversely proportional to the changing of step-size in VS-LMS algorithm. The technique reduces computational requirements of the conventional VS-LMS algorithms without a degradation of performance in convergence rate and steady state error. And a method for deriving initial step-size, when the input is zero mean, white Gaussian sequence, is proposed. For demonstrating the good performances of the proposed method, simulation results are compared with the conventional variable step-size algorithms in convergence speed and computational requirements.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.245-250
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• 2009

In communications and signal processing area, a number of least mean square adaptive algorithms have been used because of simplicity and robustness. However the LMS algorithm is known to have slow and non-uniform convergence. Various variable step size LMS adaptive algorithms have been introduced and researched to speed up the convergence rate. A variable step size LMS algorithm using the error difference for updating the step size is proposed. Compared with other algorithms, simulation results show that the proposed LMS algorithm has a fast convergence. The theoretical performance of the proposed algorithm is also analyzed for the steady state.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.15 no.6
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• pp.33-40
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• 1996

In this paper, we resolved a trade-off problem of the blind equalizer based on the stop-and-go algorithm that is commonly used for QAM demodulation in CATV receiver. The stop-and-go algorithm has used the LMS(least mean square) algorithm in the updating operation of tap weights so that the structure of equalizer is simple, but there is a trade-off between convergence speed and steady state error as in the typical LMS algorithm. We used the variable step size algrithm to improve the convergence speed with the steady state error in the constant level. With respect to the same level of the steady state error, the variable step size stop-and-go algortihm improved convergence speed by about $36%{\sim}56%$ as compared with that of the constant step size algortihm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.446-452
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• 2004

Under the condition of a common narrowband target signal and interference signals from several directions, the linearly constrained minimum variance (LCMV) method using the generalized sidelobe canceller (GSC) for adaptive beamforming has been exploited successfully However, in the case of wideband signals, the length of the adaptive filter must be extended. As a result, the complexity of the beamformer increases, which makes real-time implementation difficult. In this paper, we improve the convergence characteristics of the adaptive filter using the transform domain normalized least mean square (NLMS) approach based on the subband GSC structure without the increase of complexity. Besides, the M-MAX algorithm, which is one of various selective coefficient updating methods, is employed in order to remarkably reduce the computational cost without decreasing the convergence quality. With the combination of these methods, we propose a computationally efficient wideband adaptive beamformer and verify its efficiency through a series of simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.871-876
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• 2005

In this paper, we propose an improved partial response maximum likelihood (PRML) detection scheme that has an adaptive equalizer and can be applied in the asymmetric optical recording system with high-density. We confirmed that the proposed PRML detector improves detection performance. In addition, we implemented the detector by Verilog HDL. The adaptive equalizer is composed of tap coefficient updating unit using LMS algorithn and FIR filter. FIR filter is implemented by the transposed direct form architecture for high speed operation. Viterbi detector is implemented by the register exchange method.