Browsing by Author "Lee, Yuan Kang"

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    Empirical detection for spread spectrum and code division multiple access (CDMA) communications
    (1998) Lee, Yuan Kang; Johnson, Don H.
    In this thesis, the method of "classification with empirically observed statistics"--also known as empirical classification, empirical detection, universal classification, and type-based detection--is configured and applied to the despreading/detection receiver operation of a spread-spectrum (SS), code division multiple access (CDMA) communications system. In static and Rayleigh-fading environments, the empirical detector is capable of adapting to unknown noise environments in a superior manner than the linear matched-filter despreader/detector, and done with reasonable amounts of training. Compared to the optimum detector, when known, the empirical detector always approaches optimal performance, again, with reasonable amounts of training. In an interference-limited channel, we show that the single-user likelihood-ratio detector, which is the optimum single-user detector, can greatly outperform the matched filter in certain imperfect power-control situations. The near optimality of the empirical detector implies that it, too, will outperform the matched filter in these situations. Although the empirical detector has the added cost of requiring chip-based phase synchronization, its consistent and superior performance in all environments strongly suggests its application in lieu of the linear detector for SS/CDMA systems employing long, pseudo-random spreading. In order to apply empirical classification to digital communications, we derive the empirical forced-decision detector and show that it is asymptotically optimal over a large class of empirical classifiers.
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    Nonparametric prediction of mixing time series
    (1992) Lee, Yuan Kang; Johnson, Don H.
    Prediction of future time-series values, based on a finite set of available observations, is a prevalent problem in many branches of science and engineering. By making the assumption that the time series is either Gaussian or linear, the classical technique of linear prediction may be fruitfully applied. Unfortunately, few, if any, real-world time series are linear or Gaussian, and as such, prediction methods that can accommodate a larger class of time series are needed. In this spirit, nonparametric predictors based on the Nadaraya-Watson kernel regression estimator are examined. Using mixing conditions to quantify the dependence structure of time series, it is shown that the kernel predictor performs as well, asymptotically (in the mean square sense), as the conditional mean (optimal) predictor. In addition, a computationally efficient predictor based on the recursive kernel regression estimator is introduced. Its performance is comparable to that of the kernel predictor.