• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.922-928
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• 2007

This paper presents the fast adaptive algorithm which applies an approximation scheme into RLS algorithm. The proposed algorithm(D-RLS) derives a QRD RLS algorithm derivation process from RLS algorithm recursively. D-RLS has the similar pattern as the algorithm having the approximation that input signals are separated respectively. Computational complexity of D-RLS is O(N), fewer than $O(N^2)$. To evaluate performance of proposed algorithm, we use the system identification method of FIR and Volterra system. And, finally, we can show D-RLS has an excellent performance.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.2
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• pp.54-61
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• 2000

This paper presents the performance evaluation of channel estimation algorithm using RLS algorithm lot W-CDMA reverse link over Rayleigh fading channels. By obtaining BER(Bit Error Rate) performance through computer simulations, the RLS(Recursive Least Square) algorithm is compared with the existing WMSA(Weighted Averaging)(K=1,3) and constant gain algorithm. The channel structure, modulation and pilot patterns are applied to the ARIB (Association of Radio Industries and Business) and 3GPP (3rd Generation Partnership Project) ITU-R proposal for the IMT-2000. The BER performance of RLS algorithm with linear interpolation is similar to that of WMSA(K=1) and slightly superior to that of constant gain algorithm at low Doppler frequencies. Also, RLS algorithm performance is better than that of the WMSA(K=1,3) and constant gain algorithms about 4dB at BER=2.0$\times$$10^{-2}$ and Doppler frequencies $F_D$=320Hz. With increasing Doppler frequency, therefore, the BER performance of RLS algorithm with linear interpolation is superior to WMSA(K=L.3) and constant rain algorithms.

• The Journal of the Acoustical Society of Korea
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• v.29 no.1
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• pp.18-23
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• 2010

The RLS (Recursive Least Squares) method is a broadly used adaptive algorithm for signal processing in electronic engineering. The RLS algorithm shows a good performance and a fast adaptation within a stationary environment, but it shows a Poor performance within a non-stationary environment because the method has a fixed forgetting factor. In order to enhance 'tracking' performances, BLS methods with an adaptive forgetting factor had been developed. This method shows a good tracking performance, however, it suffers from heavy computational loads. Therefore, we propose a modified AFF-RLS which has relatively low complexity m this paper.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.38-41
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• 1991

RLS algorithms are required for applications to adaptive line enhancers, adaptive equalizers for voiceband telephone and HF modems, and wide-badn digital spectrum mobile raio in which their convergence time and tracking speed are significant. The fast QR RLS algorithm satisfies above the requirements. Its computational complexity is linearly proportional to the tap number of a filter, N and its performance remains numerically stable. From the result of simumulation, the fast QR RLS algorithm represented Cioffi is better than gradient based algorithm in its initial performance when being applied to an adaptive line enhancer for cancelling noise.

• Journal of Digital Contents Society
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• v.5 no.4
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• pp.295-299
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• 2004

In this paper, we compare and analyze the LMS ard RLS algorithm of IEEE802.15.3(HDR-WPAN) system. The LMS algorithm have two merits that easily embody and not complex, but convergence speed is slow. The RLS algorithm have fast convergence speed, but very complex. When equalization using LMS algorithm, it can achieve adaptive equalization after 250 sample in fading environment, but case of RLS algorithm can achieve adaptive equalization after just 50 sampls. The computer simulation proved that adaptive equalizer to fast equalization and stability of HDR-WPAN system is more effective using RLS algorithm then LMS algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.394-402
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• 1999

In this paper, we present an effective RLS algorithm with forgetting factor of Erlang function for the system identification. In the proposed algorithm, the forgetting factor decreases monotonically in the first stage, and then it increases monotonically in the second stage in contrary to the conventional forgetting factor RLS algorithms. In addition, annealing effect and an asymptotically stability of the proposed algorithm is discussed based on the analysis of convergency property on. Simulation results for the system identification problem indicate the superiority of the proposed algorithm in comparison to the RLS algorithm such as NLMS and Kalman filter based algorithm.

• The Journal of the Acoustical Society of Korea
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• v.15 no.3E
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• pp.78-85
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• 1996

The set of admissible time-variations in the input signal can be separated into two categories : slow parameter changes and large parameter changes which occur infrequently. A common approach used in the tracking of slowly time-varying parameters is the exponential recursive least-squares(RLS) algorithm. There have been a variety of research works on the error analysis of the exponential RLS algorithm for the slowly time-varying parameters. In this paper, the focus has been given to the error analysis of exponential RLS algorithms for the input data with abrupt property changes. The voiced speech signal is chosen as the principal application. In order to analyze the error performance of the exponential RLS algorithm, deterministic properties of the exponential RLS algorithms is first analyzed for the case of abrupt parameter changes, the impulsive input(or error variance) synchronous to the abrupt change of parameter vectors actually enhances the convergence of the exponential RLS algorithm. The analysis has also been verified through simulations on the synthetic speech signal.

• The Journal of the Acoustical Society of Korea
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• v.23 no.4E
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• pp.103-107
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• 2004

In this Paper, we propose an algorithm called partitioned recursive least square (PRLS) that involves a procedure that partitions a large data matrix into small matrices, applies RLS scheme in each of the small sub matrices and assembles the whole size estimation vector by concatenation of the sub-vectors from RLS output of sub matrices. Thus, the algorithm should be less complex than the conventional RLS and maintain an almost compatible estimation performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6B
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• pp.629-637
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• 2002

RLS a1gorithm is a kind of the adaptive a1gorithms in smart antennas and adapts the weight vector using the difference between the output signal of array antennas and the known training sequence. In this paper, we propose a new algorithm based on the RLS algorithm. It calculates the error signal with reference signal derived from blind scheme. Simulation results show that the proposed algorithm yields more user capacity by 67∼74% than other blind adaptive algorithms(LS-DRMTA, LS-DRMTCMA) at the same BER and the beamformer forms null beams toward interference signals and the main beam toward desired signal.

• 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.