The Experts below are selected from a list of 13665 Experts worldwide ranked by ideXlab platform
Ta-wei Soong - One of the best experts on this subject based on the ideXlab platform.
-
A Log-Likelihood Function-based algorithm for QAM signal classification
Signal Processing, 1998Co-Authors: Yawpo Yang, Ching-hwa Liu, Ta-wei SoongAbstract:Abstract In this paper we derive a Log-Likelihood Function-based classification algorithm for classifying quadrature amplitude modulation (QAM) signals buried in additive white Gaussian noise. We derive the amplitude density Functions of received QAM signals first, then develop the required statistics for signal classification based on the maximum a posteriori probability criterion and demonstrate a schematic structure of classifier for M -ary QAM signals. The resultant structure of this proposed classifier is shown to be flexible and easy to expand. Both the theoretical approach and the numerical approach are employed to evaluate the performance that is expressed in terms of the probability of successful classification. We also provide an example to show the capabilities of the developed classifier. It is illustrated that two approaches have consistent results and the successful classification rate reaches 100% for SNR⩾12 dB.
Yawpo Yang - One of the best experts on this subject based on the ideXlab platform.
-
Maximum Log-Likelihood Function-Based QAM Signal Classification over Fading Channels
Wireless Personal Communications, 2004Co-Authors: Yawpo Yang, Jen-ning Chang, Ji-chyun Liu, Ching-hwa LiuAbstract:In this paper, we propose a modulation classification algorithm for M-ary QAM signals in Rician and Rayleigh fading channels. The developed algorithms are based on the maximum Log-Likelihood Functions, which are derived from received signals. First of all, we derived the amplitude PDF of M-ary QAM signal over flat and slowly Rayleigh and Rician fading channel, then we developed the Log-Likelihood Functions and then the decision Functions for classification. To demonstrate the performance of the proposed classifier, we give an example to classify the 16/32 QAM signals. Results indicate that the performance of classifier is heavily dependent on the severity of channel fading. When channel is AWGN, which means that there exists only one path (may be specular path) between transmitter and receiver, and the Rician factor k, approaches infinity in this case, henceforth, the performance is the best. The performance, however, is degraded with the decrease of k, and finally the classifier performs worst when channel becomes Rayleigh. Further performance improvement can be achieved by increasing the length of record.
-
A Log-Likelihood Function-based algorithm for QAM signal classification
Signal Processing, 1998Co-Authors: Yawpo Yang, Ching-hwa Liu, Ta-wei SoongAbstract:Abstract In this paper we derive a Log-Likelihood Function-based classification algorithm for classifying quadrature amplitude modulation (QAM) signals buried in additive white Gaussian noise. We derive the amplitude density Functions of received QAM signals first, then develop the required statistics for signal classification based on the maximum a posteriori probability criterion and demonstrate a schematic structure of classifier for M -ary QAM signals. The resultant structure of this proposed classifier is shown to be flexible and easy to expand. Both the theoretical approach and the numerical approach are employed to evaluate the performance that is expressed in terms of the probability of successful classification. We also provide an example to show the capabilities of the developed classifier. It is illustrated that two approaches have consistent results and the successful classification rate reaches 100% for SNR⩾12 dB.
Ching-hwa Liu - One of the best experts on this subject based on the ideXlab platform.
-
Maximum Log-Likelihood Function-Based QAM Signal Classification over Fading Channels
Wireless Personal Communications, 2004Co-Authors: Yawpo Yang, Jen-ning Chang, Ji-chyun Liu, Ching-hwa LiuAbstract:In this paper, we propose a modulation classification algorithm for M-ary QAM signals in Rician and Rayleigh fading channels. The developed algorithms are based on the maximum Log-Likelihood Functions, which are derived from received signals. First of all, we derived the amplitude PDF of M-ary QAM signal over flat and slowly Rayleigh and Rician fading channel, then we developed the Log-Likelihood Functions and then the decision Functions for classification. To demonstrate the performance of the proposed classifier, we give an example to classify the 16/32 QAM signals. Results indicate that the performance of classifier is heavily dependent on the severity of channel fading. When channel is AWGN, which means that there exists only one path (may be specular path) between transmitter and receiver, and the Rician factor k, approaches infinity in this case, henceforth, the performance is the best. The performance, however, is degraded with the decrease of k, and finally the classifier performs worst when channel becomes Rayleigh. Further performance improvement can be achieved by increasing the length of record.
-
A Log-Likelihood Function-based algorithm for QAM signal classification
Signal Processing, 1998Co-Authors: Yawpo Yang, Ching-hwa Liu, Ta-wei SoongAbstract:Abstract In this paper we derive a Log-Likelihood Function-based classification algorithm for classifying quadrature amplitude modulation (QAM) signals buried in additive white Gaussian noise. We derive the amplitude density Functions of received QAM signals first, then develop the required statistics for signal classification based on the maximum a posteriori probability criterion and demonstrate a schematic structure of classifier for M -ary QAM signals. The resultant structure of this proposed classifier is shown to be flexible and easy to expand. Both the theoretical approach and the numerical approach are employed to evaluate the performance that is expressed in terms of the probability of successful classification. We also provide an example to show the capabilities of the developed classifier. It is illustrated that two approaches have consistent results and the successful classification rate reaches 100% for SNR⩾12 dB.
Yunpeng Zhao - One of the best experts on this subject based on the ideXlab platform.
-
A note on new Bernstein-type inequalities for the Log-Likelihood Function of Bernoulli variables
Statistics & Probability Letters, 2020Co-Authors: Yunpeng ZhaoAbstract:Abstract We prove a new Bernstein-type inequality for the Log-Likelihood Function of Bernoulli variables. In contrast to classical Bernstein’s inequality and Hoeffding’s inequality when applied to this Log-Likelihood, the new bound is independent of the parameters of the Bernoulli variables and therefore does not blow up as the parameters approach 0 or 1. The new inequality strengthens certain theoretical results on Likelihood-based methods for community detection in networks and can be applied to other Likelihood-based methods for binary data.
-
A Note on New Bernstein-type Inequalities for the Log-Likelihood Function of Bernoulli Variables
arXiv: Probability, 2019Co-Authors: Yunpeng ZhaoAbstract:We prove a new Bernstein-type inequality for the Log-Likelihood Function of Bernoulli variables. In contrast to classical Bernstein's inequality and Hoeffding's inequality when applied to the Log-Likelihood, the new bound is independent of the parameters of the Bernoulli variables and therefore does not blow up as the parameters approach 0 or 1. The new inequality strengthens certain theoretical results on Likelihood-based methods for community detection in networks and can be applied to other Likelihood-based methods for binary data.
Robin Thompson - One of the best experts on this subject based on the ideXlab platform.
-
A note on bimodality in the Log-Likelihood Function for penalized spline mixed models
Computational Statistics & Data Analysis, 2009Co-Authors: S. J. Welham, Robin ThompsonAbstract:For a smoothing spline or general penalized spline model, the smoothing parameter can be estimated using residual maximum Likelihood (REML) methods by expressing the spline in the form of a mixed model. The possibility of bimodality in the profile Log-Likelihood Function for the smoothing parameter of these penalized spline mixed models is demonstrated. A canonical transformation into independent observations is used to provide efficient evaluation of the Log-Likelihood Function and gives insight into the incompatibilities between the model and data that cause bimodality. This transformation can also be used to assess the influence of different frequency components in the data on the estimated smoothing parameter. It is demonstrated that, where bimodality occurs in the Log-Likelihood, Bayesian penalized spline models may show poor mixing in MCMC chains and be sensitive to the choice of prior distributions for variance components.
-
AnoteonbimodalityintheLog-LikelihoodFunctionforpenalizedspline mixedmodels
2009Co-Authors: S. J. Welham, Robin ThompsonAbstract:a b s t r a c t For a smoothing spline or general penalized spline model, the smoothing parameter can be estimated using residual maximum Likelihood (REML) methods by expressing the spline in the form of a mixed model. The possibility of bimodality in the profile Log-Likelihood Function for the smoothing parameter of these penalized spline mixed modelsisdemonstrated.Acanonicaltransformationintoindependentobservationsisused to provide efficient evaluation of the Log-Likelihood Function and gives insight into the incompatibilitiesbetweenthemodelanddatathatcausebimodality.Thistransformation
