• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.9
• /
• pp.4326-4341
• /
• 2016

In this paper, an adjustable multiple relay selection (MRS) scheme for cooperative communication with amplify-and-forward (AF) relay under frequency selective channels is proposed. In the proposed scheme, the relays are ordered firstly by the steady-state mean square error (MSE), then the relays are sequentially selected out from N relays and the number of cooperating relays is adjusted dynamically according to the steady-state mean square joint error (MSJE). The aim of this work is to dynamically estimate the optimum number N_o of cooperating relays. Optimum means the minimum number of cooperating relays, N_o, achieving the minimum level of steady-state MSJE. Numerical results verify the analyses and show that the scheme can adaptively adjust the number of cooperating relays, and outperform conventional relay selection schemes. Hence, the proposed scheme provides better tradeoff between BER performance and spectral efficiency and to save more energy in cooperative wireless networks.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.6
• /
• pp.691-696
• /
• 2018

We propose an algorithm to update weight to use the mean square error method and mutual coupling matrix in a coherent channel. The algorithm proposed in this paper estimates the desired signal by using the updated weight. The updated weight is obtained by covariance matrix using mean square error and mutual coupling matrix. The MUSIC algorithm, which is direction of arrival estimation method, is mostly used in the desired signal estimation. The MUSIC algorithm has a good resolution because it uses subspace techniques. The proposed method estimates the desired signal by updating the weights using the mutual coupling matrix and mean square error method. Through simulation, we analyze the performance by comparing the classical MUSIC and the proposed algorithm in a coherent channel. In this case of the coherent channel for estimating at the three targets (-10o, 0o, 10o), the proposed algorithm estimates all the three targets (-10o, 0o, 10o). But the classical MUSIC algorithm estimates only one target (x, x, 10o). The simulation results indicate that the proposed method is superior to the classical MUSIC algorithm for desired signal estimation.

• Communications for Statistical Applications and Methods
• /
• v.20 no.5
• /
• pp.357-366
• /
• 2013

This paper defines an improvement for estimating the population mean of a study variable using auxiliary information and known values of certain population parameter(s), when there is a non-response in a study as well as on auxiliary variables. Under a simple random sampling without a replacement (SRSWOR) scheme, the mean square error (MSE) of all proposed estimators are obtained and compared with each other. Numerical illustration is also given.

• Journal of Communications and Networks
• /
• v.13 no.6
• /
• pp.621-624
• /
• 2011

With the rapid growth of internet traffic, accurate and reliable prediction of internet traffic has been a key issue in network management and planning. This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) error model for forecasting internet traffic and evaluates its performance by comparing it with seasonal autoregressive integrated moving average (ARIMA) models in terms of root mean square error (RMSE) criterion. The results indicated that the seasonal AR-GARCH models outperformed the seasonal ARIMA models in terms of forecasting accuracy with respect to the RMSE criterion.

• Journal of the Korean Data and Information Science Society
• /
• v.1
• /
• pp.47-57
• /
• 1990

A method which determines the number of replications in the simulation is proposed, particularly for small-sample comparison of estimators. This method takes the smallest number of replications that makes the difference of mean square errors be statistically significant and provides an efficient algorithm for calculating the standard error of the mean square error. Two examples are illustrated, the first one is on comparison of mean and median ; the second, the Kaplan-Meier type and Buckley-James type estimators of a quantile function with censored data.

• Journal of Biosystems Engineering
• /
• v.12 no.2
• /
• pp.38-43
• /
• 1987

An experimental study was conducted to develop a thin-layer rewetting equation of short grain rough rice of Akihikari variety. Four thin-layer rewetting equations were experimentally determined from $25^{\circ}C$ to $45^{\circ}C$ and 70%RH to 85%RH conditions. Diffusion, Henderson, Page, and Thompson equations widely used as thin-layer drying equations were selected. Experimental data were fitted to these equations using linear regression analysis except diffusion equation. The diffusivity in the diffusion equation was determined by optimization method. Four equations were highly significant. In order to compare the goodness of fit of each equation, the error mean square of each equawas calculated. The diffusion model was not a very good model because the error mean square was very large. The other three models showed the same level or error mean square and could predict satisfactorily the rewetting rate or short grain rough rice.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.7A
• /
• pp.529-536
• /
• 2003

In this Paper, a new DCT HLMS DFE(Discrete Cosine Transform Hierarchical Least Mean Square Decision Feedback Equalizer) algorithm is proposed to improve the convergence speed and MSE(Mean Square Error) performance of a receive channel equalizer in ATSC(Advanced Television System Committee) 8VSB(Vestigial Side Band) digital terrestrial TV system. The proposed algorithm reduces the eigenvalue range of input data autocorrelation by transforming LMS (Least Mean Square) DFE into the subfilter of hierarchical structure. Moreover, the use of DCT and power estimation algorithm makes it possible to reduce the eigenvalue deviation of input data which results from distortion and delay of the receive signal in the miulti-path environment. Simulation results show that proposed DCT HLMS DFE has SNR improvement of approximately 3.8dB, 5dB and 2dB as compared to LMS DFE when the equalized symbol error rate is 0.2 in ATTC defined digital terrestrial TV broadcasting channels A, B and F, respectively.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.19 no.9
• /
• pp.1058-1064
• /
• 2008

In this paper, we proposed a new hybrid interference canceller using the adaptive filter with Grouped CMA(Constant Modulus Algorithm)-LMS(Least Mean Square) algorithm in the RF(Radio Frequency) repeater. The feedback signal generated from transmitter antenna to receiver antenna reduces the performance of the receiver system. The proposed interference canceller has better channel adaptive performance and a lower MSE(Mean Square Error) than conventional structure because it uses the cancellation method of Grouped CMA algorithm. This structure reduces the number of iterations fur the same MSE performance and hardware complexity compared to conventional nonlinear interference canceller. Namely, MSE values of the proposed algorithm were lower than those of LMS algorithm by 2.5 dB and 4 dB according to step sizes. And the proposed algorithm showed fast speed of convergence and similar MSE performance compared to VSS(Variable Step Size)-LMS algorithm.

• Survey Research
• /
• v.12 no.3
• /
• pp.49-62
• /
• 2011

In this paper we investigate design-based properties of both the ordinary least square estimator and the weighted least square estimator for regression coefficients in panel regression model. We derive formulas of approximate bias, variance and mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator after linearization of least square estimators. Also we compare their magnitudes each other numerically through a simulation study. We consider a three years data of Korean Welfare Panel Study as a finite population and take household income as a dependent variable and choose 7 exploratory variables related household as independent variables in panel regression model. Then we calculate approximate bias, variance, mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator based on several sample sizes from 50 to 1,000 by 50. Through the simulation study we found some tendencies as follows. First, the mean square error of the ordinary least square estimator is getting larger than the variance of the weighted least square estimator as sample sizes increase. Next, the magnitude of mean square error of the ordinary least square estimator is depending on the magnitude of the bias of the estimator, which is large when the bias is large. Finally, with regard to approximate variance, variances of the ordinary least square estimator are smaller than those of the weighted least square estimator in many cases in the simulation.

• The Journal of the Acoustical Society of Korea
• /
• v.35 no.1
• /
• pp.49-54
• /
• 2016

In this paper, we propose a vocal separation method using weighted ${\beta}$-order minimum mean wquare error estimation (WbE) based on kernel back-fitting algorithm. In spoken speech enhancement, it is well-known that the WbE outperforms the existing Bayesian estimators such as the minimum mean square error (MMSE) of the short-time spectral amplitude (STSA) and the MMSE of the logarithm of the STSA (LSA), in terms of both objective and subjective measures. In the proposed method, WbE is applied to a basic iterative kernel back-fitting algorithm for improving the vocal separation performance from monaural music signal. The experimental results show that the proposed method achieves better separation performance than other existing methods.