The Experts below are selected from a list of 13149 Experts worldwide ranked by ideXlab platform
Hyuckjae Lee - One of the best experts on this subject based on the ideXlab platform.
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signal detection using Log Likelihood Ratio based sorting qr decomposition for v blast systems
Vehicular Technology Conference, 2007Co-Authors: Hyunseok Lee, Hyoungsuk Jeon, Hoiyoon Jung, Hyuckjae LeeAbstract:In this paper, we propose a novel detection algorithm using Log-Likelihood Ratio (LLR) to detect signals for V-BLAST systems. This algorithm utilizes the sorting QR decomposition (SQRD) of the channel matrix, and applies LLR to determine the order of detection. Simulation results show that the proposed LLR-based MMSE-SQRD algorithm provides a better performance than the conventional SNR-based MMSE-SQRD algorithm with a few additional computations. Approximately, the average BER performance of our algorithm is better than that of the conventional SNR-based MMSE-SQRD algorithm by 4 dB at 10-4 target BER.
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a novel detection algorithm using the sorted qr decomposition based on Log Likelihood Ratio in v blast systems
International Conference on Wireless Communications Networking and Mobile Computing, 2006Co-Authors: Hyunseok Lee, Hyoungsuk Jeon, Jihwan Choi, Wonsop Kim, Jongsub Cha, Hyuckjae LeeAbstract:In this paper, we propose a novel detection algorithm using Log-Likelihood Ratio (LLR) to detect signals in V-BLAST systems. This algorithm utilizes the sorted QR decomposition (SQRD) of the channel matrix, and applies LLR to determine the order of detection. Simulation results show that the proposed algorithm provides a better performance than the conventional SQRD with a few additional computations. Approximately, the average BER performance of our algorithm is better than that of the conventional SQRD algorithm by 6 dB for BPSK and by 2 dB for QPSK respectively at 10-3 target BER
Sang Wu Kim - One of the best experts on this subject based on the ideXlab platform.
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Log-Likelihood-Ratio-based detection ordering in V-BLAST
IEEE Transactions on Communications, 2006Co-Authors: Sang Wu Kim, Kyeong Pyo KimAbstract:We propose a new detection ordering based on the Log-Likelihood Ratio (LLR) in the iterative nulling and cancellation process of vertical Bell Laboratories layered space-time (V-BLAST) decoding. The motivation for using the LLR is that it provides the reliability information on the maximum a posteriori probability decision. As a result, the error propagation associated with a wrong cancellation can be minimized. Simplified ordering schemes that require a much less computation, but provide a performance virtually identical to the LLR-based ordering, are also provided. The performance of the LLR ordering in the V-BLAST combined with space-time block codes is evaluated.
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Generalized selection combining based on the Log-Likelihood Ratio
IEEE Transactions on Communications, 2004Co-Authors: Sang Wu Kim, Young Gil Kim, Marvin K. SimonAbstract:We propose a generalized selection combining (GSC) scheme for binary signaling in which a subset of diversity branches providing the largest magnitude of Log-Likelihood Ratio (LLR) are selected and combined. It is shown that the bit-error probability with maximum Ratio combining (MRC) or square-law combining of L branches is identical to that with LLR-based GSC of L/2 branches. We also propose a simple, but suboptimal, GSC based on a noncoherent envelope detection and discuss its potential advantages over the conventional signal-to-noise-Ratio-based GSC and MRC.
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Log Likelihood Ratio based detection ordering for the v blast
Global Communications Conference, 2003Co-Authors: Sang Wu KimAbstract:We propose a new detection ordering for the V-BLAST. The main idea is to detect and cancel sub-streams in order of the magnitude of Log-Likelihood Ratio (LLR), i.e. the symbol with the largest magnitude of LLR is detected first. The motivation is that the reliability of data decision increases with increasing magnitude of LLR. As a result, the error propagation associated with a wrong decision and the resulting error probability for the remaining sub-streams can be minimized. It is shown that the proposed LLR-based ordering significantly outperforms the conventional SNR-based ordering. Simplified LLR-based ordering and envelope-based ordering that require a much less computation, but provide a performance virtually identical to the LLR-based ordering, are also proposed.
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generalized selection combining based on the Log Likelihood Ratio
International Conference on Communications, 2003Co-Authors: Sang Wu Kim, Young Gil Kim, Marvin K. SimonAbstract:We propose a generalized selection combining (GSC) scheme for binary signaling in which M diversity branches providing the largest magnitude of Log-Likelihood Ratio (LLR) are selected and combined. The bit error probability provided by LLR-based GSC serves as a lower bound on the bit error probability provided by any GSC techniques. We also propose a suboptimal GSC based on a noncoherent envelope detection. We derive the bit error probability with LLR-based and envelope-based GSC techniques and examine their power gains over the conventional SNR-based GSC technique. We show that the bit error probability with maximum Ratio combining or square-law combining of L branches is identical to that with LLR-based GSC of L/2 branches.
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Successive interference cancellation in CDMA systems: Log-Likelihood Ratio approach
MILCOM 2005 - 2005 IEEE Military Communications Conference, 1Co-Authors: Sang Wu Kim, Young Jun HongAbstract:We propose a new successive interference cancellation (SIC) scheme that cancels the interference in order of the magnitude of Log-Likelihood Ratio (LLR) in CDMA systems. The motivation is that it provides the reliability information on the maximum a posteriori decision. As a result, the error propagation associated with a wrong data detection can be minimized and the resulting error probability for the remaining users can be reduced. We show that the LLR-ordered SIC outperforms the traditional SNR-ordered SIC significantly. Non-binary signaling and the effect of channel estimation errors are discussed
Lawrence R. Rabiner - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - On the measurement of waveform coder distortion using the Log Likelihood Ratio
ICASSP '80. IEEE International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: Ronald Eldon Crochiere, José Tribolet, Lawrence R. RabinerAbstract:The Log Likelihood measure has been widely used in speech recognition for comparing speech signals. Recently it has been proposed as a measure for assessing the quality of coded speech. In this paper we present an interpretation of the Log Likelihood Ratio measure within the theoretical framework of a waveform coder distortion model.
Yajun Mei - One of the best experts on this subject based on the ideXlab platform.
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Quantization Effect on the Log-Likelihood Ratio and Its Application to Decentralized Sequential Detection
IEEE Transactions on Signal Processing, 2013Co-Authors: Yan Wang, Yajun MeiAbstract:It is well known that quantization cannot increase the Kullback-Leibler divergence which can be thought of as the expected value or first moment of the Log-Likelihood Ratio. In this paper, we investigate the quantization effects on the second moment of the Log-Likelihood Ratio. It is shown via the convex domination technique that quantization may result in an increase in the case of the second moment, but the increase is bounded above by 2/e. The result is then applied to decentralized sequential detection problems not only to provide simpler sufficient conditions for asymptotic optimality theories in the simplest models, but also to shed new light on more complicated models. In addition, some brief remarks on other higher-order moments of the Log-Likelihood Ratio are also provided.
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ISIT - Quantization effect on second moment of Log-Likelihood Ratio and its application to decentralized sequential detection
2012 IEEE International Symposium on Information Theory Proceedings, 2012Co-Authors: Yan Wang, Yajun MeiAbstract:It is well known that quantization cannot increase the Kullback-Leibler divergence which can be thought of as the expected value or first moment of the Log-Likelihood Ratio. In this paper, we investigate the quantization effects on the second moment of the Log-Likelihood Ratio. It is shown that quantization may result in an increase in the case of the second moment, but the increase is bounded above by 2/e. The result is then applied to decentralized sequential detection problems to provide a simpler sufficient condition for asymptotic optimality theory, and the technique is also extended to investigate the quantization effects on other higher-order moments of the Log-Likelihood Ratio and provide lower bounds on higher-order moments.
Augustine C. M. Wong - One of the best experts on this subject based on the ideXlab platform.
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On standardizing the signed root Log Likelihood Ratio statistic
Statistics & Probability Letters, 2012Co-Authors: L. Jiang, Augustine C. M. WongAbstract:A simple connection between the Bartlett adjustment factor of the Log Likelihood Ratio statistic and the normalizing constant of the p∗ formula–an approximate conditional density for the maximum Likelihood estimate given an exact or an approximate ancillary statistic–was established in Barndorff-Nielsen and Cox (1984). In this paper, the explicit form of the normalizing constant of the p∗ formula for the scalar parameter model is derived. By change of variables, the mean and variance of the signed root Log Likelihood Ratio statistic are obtained explicitly, and, hence, tail probabilities can be calculated from the standardized signed root Log Likelihood Ratio statistic. Examples are used to illustrate the implementation and accuracy of the proposed method.
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Approximating the F distribution via a general version of the modified signed Log-Likelihood Ratio statistic
Computational Statistics & Data Analysis, 2008Co-Authors: Augustine C. M. WongAbstract:A simple normal approximation for the cumulative distribution function of the F distribution is obtained via a general version of the modified signed Log-Likelihood Ratio statistic. This approximation exhibits remarkable accuracy even when the degrees of freedom are small. Using the same methodoLogy, but with a simpler set up, simple and accurate normal approximations to the cumulative distribution functions of the Student t and @g^2 distributions can also be obtained.
