• ETRI Journal
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• v.45 no.3
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• pp.379-393
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• 2023

Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct-adaptive filtering algorithms have become popular in UWC. Herein, we present an improved adaptive convex-combination method for the identification of sparse structures using a reweighted normalized leastmean-square (RNLMS) algorithm. Moreover, to make RNLMS algorithm independent of the reweighted l₁-norm parameter, a modified sparsity-aware adaptive zero-attracting RNLMS (AZA-RNLMS) algorithm is introduced to ensure accurate modeling. In addition, we present a quantitative analysis of this algorithm to evaluate the convergence speed and accuracy. Furthermore, we derive an excess mean-square-error expression that proves that the AZA-RNLMS algorithm performs better for the harsh underwater channel. The measured data from the experimental channel of SPACE08 is used for simulation, and results are presented to verify the performance of the proposed algorithm. The simulation results confirm that the proposed algorithm for underwater channel estimation performs better than the earlier schemes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.11
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• pp.1714-1725
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• 1993

This paper analyzes the properties of such algorithm that corresponds to the nonlinear adaptive algorithm with additional update terms, parameterized by the scalar factors a1, a2, a3 and Presents its structure. The analysis of convergence leads to eigenvalues of the transition matrix for the mean weight vector. Regions in which the algorithm becomes stable are demonstrated. The time constant is derived and the computational complexities of MLMS algorithms are compared with those of the conventional LMS, sign, LFG, and QFG algorithms. The properties of convergence in the mean square are analyzed and the expressions of the mean square recursion and the excess mean square error are derived. The necessary condition for the CFG algorithm to be stable is attained. In the computer simulation applied to the system identification the CFG algorithm has the more computation complexities but the faster convergence speed than LMS, LFG and QFG algorithms.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8C
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• pp.756-763
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• 2009

The Least Mean Square (LMS) algorithm is often used to restore signal corrupted by additive noise. A major defect of this algorithm is that the excess Mean Square Error (EMSE) increases linearly according to speech signal power. This result reduces the efficiency of performance significantly due to the large EMSE around the optimum value. Choosing a small step size solves this defect but causes a slow rate of convergence. The step size must be optimized to satisfy a fast rate of convergence and minimize EMSE. In this paper, the Instantaneous Gain Control (IGC) algorithm is proposed to deal with the situation as it exists in speech signals. Simulations were carried out using a real speech signal combined with Gaussian white noise. Results demonstrate the superiority of the proposed IGC algorithm over the LMS algorithm in rate of convergence, noise reduction and EMSE.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3C
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• pp.338-347
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• 2007

In this paper, a weighted block adaptive channel estimation (WBA-CE) for a space-time block-coded (STBC) single-carrier transmission with a cyclic-prefix is proposed. In operation of the WBA-CE, a STBC matrix-wise block for filter input symbols is first formulated. Applying a weighted a posteriori error vector-based least-square (LS) criterion for this block, the coefficient correction terms of the WBA-CE are then computed. An approximate steady-state excess mean-square error (EMSE) of the WBA-CE for the stationary optimal coefficient is also analyzed. Simulation results show in a time-varying typical urban (TU) channel that the proposed channel estimator provides better bit-error-rate (BER) performances than conventional algorithms such as the NLMS and RLS channel estimators.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.1
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• pp.131-142
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• 1994

This paper analyzes the properties of such algorithm that corresponds to the nonlinear adaptive algorithm with additional update terns, parameterized by the scalar factors ${\alpha}1,\;and\;{\alpha}2$. The analysis of concergence leads to eigenvalues of the transition matrix for the mean filter coefficient vector. Regions in which the algorithm becomes stable are demonstrated. The time constant is derived and the computational complexity of the QFG algorithm is compared with those of the conventional LMS. sign, and LFG algorithm. The properties of convergence in the mean square error is derived and the neccessary condition for the CFG algorithm to be stable is attaned. In the computer simulation a channel equalization is utilized to demonstrate the performance feature of the QFG algorithm. The QFG algorithm has the more computational complexities but the faster convergence speed than LMS and LFG algorithm. Since the QFG algorithm has smoother convergence, it may be useful in case where error bursting is a problem.

• Journal of electromagnetic engineering and science
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• v.4 no.1
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• pp.1-7
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• 2004

In this paper the Individual tap - Least Mean Square(IT-LMS) algorithm is applied to the adaptive multipath channel equalization using hybrid-type Genetic Algorithm(GA) for achieving lower minimum Mean Squared Error(MSE). Owing to the global search performance of GA, LMS-type equalizers combined with it have shown preferable performance in both global and local search but those still have unsatisfying minimum MSE performance. In order to lower the minimum MSE we investigated excess MSE of IT-LMS algorithm and applied it to the hybrid GA equalizer. The high convergence rate and lower minimum MSE of the proposed system give us reason to expect that it will perform well in practical multi-path channel equalization systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3C
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• pp.183-189
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• 2011

The LMS(Least Mean Square) algorithm is generally used because of tenacity, high mating spots and simplicity of realization. But the LMS algorithm has trade-off between nonuniform collect and EMSE(Excess Mean Square Error). To overcome this weakness, variable step size is used widely but it needs a lot of calculation load. In this paper we consider new algorithm, which can reduce calculations and adapt in case of environment changes, uses original signal and noise signal of IGC(Instantaneous Gain Control). For the real time processing of IGC algorithm, we remove the logarithmic function. The performance of proposed algorithm is tested to adaptive noise canceller in automobile. We show implemented LabVIEW FPGA system of IGC algorithm is more efficient than others.

• The Journal of the Acoustical Society of Korea
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• v.7 no.2
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• pp.20-25
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• 1988

This study is to explain the characteristic of excess attenuation on the ground through the outdoors experiment about noise propagation and the reduced model experiment of acoustic. The outdoors experiment on the attenuation of noise propagation was tried with the small engine that had large acoustic output, and then it was conformed that there was relationship between the excess attenuation calculated by measurement from distance attenuation and Log(D/(Hs+Hr)). As a result, it was found that the attenuation of noise propogation depended upon the direction of the wind and frequency and was regressed in a straight line. And the numerical values of excess attenuation on the ground could be calculated by regarding Log(D/(Hs+Hr)) as a parameter with an airing resistance $\sigma$. It was found that when the mean square error between the excess attenuation calculated by measurement and the value calculated by a fomula $L=-20Log\mid1+(r_1/r_2)Qexp(ik, \bigtriangleup r)\mid$ about optional $\sigma$ was least, the optimal decision of u was made. As the characteristic of model is the model experiment on a reduced scale of 1 to 40, It was conformed that it corresponds enough with the measurement value with measuring the distance attenuation in the large anecoic chamber.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.42-46
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• 2018

Adaptive signal processing is quite important in various signal and communication environments. In adaptive signal processing methods since the least mean square(LMS) algorithm is simple and robust, it is used everywhere. As the step is varied in the variable step(VS) LMS algorithm, the fast convergence speed and the small excess mean square error can be obtained. Various variable step LMS algorithms are researched for better performances. But in some of variable step LMS algorithms the computational complexity is quite large for better performances. The fixed step LMS algorithm with a low computational complexity merit and the variable step LMS algorithm with a fast convergence merit are combined in the proposed sporadic step algorithm. As the step is sporadically updated, the performances of the variable step LMS algorithm can be maintained in the low update rate using Fibonacci sequence. The performances of the proposed variable step LMS algorithm are proved in the adaptive equalizer.