|
|
|
“Fading Channel Equalization and Video Traffic Classification Using Nonlinear Signal Processing Techniques”
by Qilian Liang
May 2000
This dissertation presents some new approaches for fading channel equalization and video traffic classification. Since there exists uncertainties of the received signals in fading channels and frame sizes of video traffic, we propose a new nonlinear signal processing technique, interval type-2 fuzzy logic systems, which can handle these uncertainties.
We apply an unnormalized interval type-2 TSK FLS as a type-2 fuzzy adaptive filter (FAF) to equalization of nonlinear and fading channels, and demonstrate that it can implement the Bayesian equalizer for such a channel, has a simple structure, and provides fast inference. Two structures are used for the Bayesian equalizer respectively: transversal and decision feedback. We propose a decision tree structure to implement the decision feedback equalizer (DFE), and each leaf of the tree is a type-2 FAF.
This DFE vastly reduces the computational complexity compared to a transversal equalizer (TE). Simulation results show that Bayesian equalizers based on type-2 FAFs performs much better than nearest neighbor classifiers (NNC) and the Bayesian equalizers based on type-1 FAFs. We also present a method for overcoming time-varying co-channel interference (CCI) using type-2 FAF.
We apply unnormalized interval type-2 Mamdani FLSs as type-2 fuzzy classifiers (FC) to MPEG VBR video traffic modeling and classification. We demonstrate that a type-2 fuzzy membership function, i.e., a Gaussian MF with uncertain std, is most appropriate to model the log-value of I/P/B frame sizes in MPEG VBR video. The fuzzy c-means (FCM) method is used to obtain the mean and standard deviation (std) of I/P/B frame sizes when the frame category is unknown. Five fuzzy classifiers and a Bayesian classifier are designed for video traffic classification, and the fuzzy classifiers are compared against the Bayesian classifier. Simulation results show that a type-2 fuzzy classifier performs the best of the six classifiers.
Finally, we present our conclusions and some directions for future research.