• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11C
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• pp.1025-1028
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• 2009

This paper presents upper bounds of Maximum Likelihood (ML) decoding performance of a few irregular LDPC codes using the simple bound and ML input output weight distributions and it is shown that contrary to general opinion that as block length becomes longer, BP decoding performance becomes simply closer to ML decoding performance, before peak degradation, as block length becomes longer, BP decoding performance falls behind ML decoding performance more and after peak degradation, general opinion holds.

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.371-383
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• 2019

Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.

• Journal of Communications and Networks
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• v.8 no.2
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• pp.205-211
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• 2006

This paper considers the maximum likelihood (ML) detection of 16-ary differential amplitude and phase shift keying (DAPSK) in Rayleigh fading channels. Based on the conditional likelihood function, two new receiver structures, namely ML symbol-by-symbol receiver and ML sequence receiver, are proposed. For the symbol-by-symbol detection, the conventional DAPSK detector is shown to be sub-optimum due to the complete separation in the phase and amplitude detection, but it results in very close performance to the ML detector provided that its circular amplitude decision thresholds are optimized. For the sequence detection, a simple Viterbi algorithm with only two states are adopted to provide an SNR gain around 1 dB on the amplitude bit detection compared with the conventional detector.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.11A
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• pp.1063-1071
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• 2008

This paper proposes a prior maximum likelihood (ML) detection verifier which has an ability to verify if the zero forcing (ZF) detection results are identical to the ML detection results. Since more than 90% of ZF detection results are identical to ML detection results, the proposed verifier makes it possible to omit the computationally complex ML detection in 90% cases of MIMO signal detections. The proposed verifier is designed by using the diversity gain obtained from converting MIMO signal into single input multiple output (SIMO) signals. In the proposed method, single input multiple output (SIMO) signals for each transmit antenna are separated from MIMO signals after the MIMO signals are detected by ZF method. Computer simulations show that the true alarm probability of the proposed verifier is more than 80% and the false alarm probability is less than $10^{-4}$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11A
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• pp.1167-1175
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• 2006

The ML-DFE(Maximum Likelihood-Decision Feedback Equalization) can be viewed as either a suboptimal signal detection method for reducing hardware complexity of ML or an enhanced detection method for reducing the effect of error propagation of SIC(Successive Interference Cancellation) in spatially multiplexed MIMO systems such as V-BLAST. The ML-DFE can achieve a higher diversity in rich scattering environments as well as reducing the error propagation effect by combing ML decoding with the DFE. In this paper, an MML-DFE(Modified Maximum Likelihood-Decision Feedback Equalization) is proposed to reduce the hardware complexity of the ML-DFE, without compromising performance. It is shown by FPGA implementation that the proposed MML-DFE can achieve the same performance as the ML-DFE with significantly reduced hardware complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.7C
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• pp.616-626
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• 2007

In spatially multiplexed MIMO systems that enable high data rate transmission over wireless communication channels, the spatial demultiplexing at the receiver is a challenging task, and various demultiplexing methods have been developed recently by many researchers. Among the previous methods, maximum likelihood detection with QR decomposition and M-algorithm (QRM-MM)), and sphere decoding (SD) schemes have been reported to achieve a (near) maximum likelihood (ML) performance. In this paper, we propose a novel signal detection method that achieves a near ML performance in a computationally efficient manner. The proposed method is demonstrated via a set of computer simulations that the proposed method achieves a near ML performance while requiring a complexity that is comparable to that of the conventional MMSE-OSIC. We also show that the log likelihood ratio (LLR) values for all bits are obtained without additional calculation but as byproduct in the proposed detection method, while in the previous QRM-MLD, SD, additional computation is necessary after the hard decision for LLR calculation.

• The Journal of the Acoustical Society of Korea
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• v.13 no.4
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• pp.20-31
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• 1994

The maximum likelihood(ML) estimation has excellent accuracy for frequency estimation of multiple sinusoids, but the maximum likelihood function requires much loss owing to the high nonlinearity. This paper presents a simplified maximum likelihood estimation, in order to improve the nonlinearity of the maximum likelihood estimation for frequencies of sinusoids in signals. This method is applied to the frequency estimation of sinusoidal signals corrupted by white or colored measurement noise. Monte-carlo simulations are conducted for the comparison of ML method with the best MFBLP method, in terms of sampled mean, root mean square and relative bias. The power spectral density and the position of frequency in unit circle are appeared in figures.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.26-35
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• 2014

We consider multiple-input multiple-output decode-and-forward relay systems in Rayleigh fading channels under the partial channel state information (CSI) that the channel statistics of the source-relay (SR) link and the instantaneous CSI of the source-destination and relay-destination links are known at the destination. In this paper, we propose a new near maximum likelihood (near-ML) decoder with two-level pairwise error probability (near-ML-2PEP) which uses the average PEP instead of the exact PEP. Then, we theoretically prove that the near-ML and near-ML-2PEP decoders achieve the maximum diversity, which is confirmed by Monte Carlo simulations. Moreover, we show that the near-ML-2PEP decoder can also achieve the maximum diversity by substituting the average PEP with the values that represent the error performance of the SR link.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.981-989
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• 2015

In a log likelihood ratio(LLR) calculation of the detected symbol, multiple-input multiple-output(MIMO) system applying an optimal or suboptimal algorithm such as a maximum likelihood(ML) detection, sphere decoding(SD), and QR decomposition with M-algorithm Maximum Likelihood Detection(QRM-MLD) suffers from exponential complexity growth with number of spatial streams and modulation order. In this paper, we propose a LLR calculation method with very low complexity in the QRM-MLD based symbol detector for a high order modulation based $N_T{\times}N_R$ MIMO system. It is able to approach bit error rate(BER) performance of full maximum likelihood detector to within 1 dB. We also analyze the BER performance through computer simulation to verify the validity of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12C
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• pp.1082-1087
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• 2008

Maximum Likelihood (ML) detection is well known to exhibit better bit-error-rate (BER) than many other detectors for multiple-input multiple-output (MIMO) channel. However, ML detection has been shown a difficult problem due to its NP-hard problem. It means that there is no known algorithm which can find the optimal solution in polynomial-time. In this paper, Semi-Definite relaxation (SDR) is iteratively applied to ML detection problem. The probability distribution can be obtained by survival eigenvector out of the dominant eigenvalue term of the optimal solution. The probability distribution which is yielded by SDR is recurred to the received signal. Our approach can reach to nearly ML performance.