• 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.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 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9C
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• pp.882-888
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• 2006

Multiuser detection can significantly increase system capacity and improve service quality compared with the existing matched filter. In this paper, we introduce an method which efficiently calculates the maximum likelihood (ML) metric based on the gradient search (GS). The ML detection needs user powers as well as their spreading codes. A method is also proposed that allows us to detect data bits with the estimation of user powers when they are unknown. Computer simulation shows that the proposed method can nearly achieve the same performance as the GS with perfectly hewn user powers.

• 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.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.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.280-284
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• 2008

In this paper, a best channel matrix selection scheme(BCMS) is proposed to approximate maximum likelihood(ML) detection for a multiple-input multiple-output system. For a one stage BCMS scheme, one of the transmitted symbols is selected to perform ML detection and the other symbols are detected by zero forcing(ZF). To increase the diversity of the symbols that are detected by ZF, multi-stage BCMS detection scheme is used to further improve the system performance. Simulation results show that the performance of the proposed BCMS scheme can approach that of ML detection with a significant reduction in complexity.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.11
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• pp.1-8
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• 2008

In this paper, we analyze the symbol error rate (SER) performance of maximum likelihood (ML) joint detection in downlink multiple-input multiple-output (MIMO) multicarrier code division multiple access (MC-CDMA) systems by deriving a tight union bound on the symbol error rate (SER). The union bound for ML joint detection is utilized to demonstrate the performance of MIMO MC-CDMA systems quantitatively in multiuser and frequency selective Rayleigh fading environments. An analysis of the diversity order of the systems shows the effects of multiple users, spread subcarriers, and multiple antennas on the ML joint detection performance. Furthermore, the analysis shows that MIMO MC-CDMA systems without full loading can achieve more diversify than MIMO orthogonal frequency division multiplexing (OFDM) systems.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1016-1026
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• 2017

An adaptive extended Kalman filter based on the maximum likelihood (EKF-ML) is proposed for detecting voltage sag in this paper. Considering that the choice of the process and measurement error covariance matrices affects seriously the performance of the extended Kalman filter (EKF), the EKF-ML method uses the maximum likelihood method to adaptively optimize the error covariance matrices and the initial conditions. This can ensure that the EKF has better accuracy and faster convergence for estimating the voltage amplitude (states). Moreover, without more complexity, the EKF-ML algorithm is almost as simple as the conventional EKF, but it has better anti-disturbance performance and more accuracy in detection of the voltage sag. More importantly, the EKF-ML algorithm is capable of accurately estimating the noise parameters and is robust against various noise levels. Simulation results show that the proposed method performs with a fast dynamic and tracking response, when voltage signals contain harmonics or a pulse and are jointly embedded in an unknown measurement noise.