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Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters

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2 Author(s)
Qilian Liang ; Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA, USA ; Mendel, J.M.

Presents a kind of adaptive filter: type-2 fuzzy adaptive filter (FAF); one that is realized using an unnormalized type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic system (FLS). We apply this filter to equalization of a nonlinear time-varying channel and demonstrate that it can implement the Bayesian equalizer for such a channel, has a simple structure, and provides fast inference. A clustering method is used to adaptively design the parameters of the FAF. Two structures are used for the equalizer: transversal equalizer (TE) and decision feedback equalizer (DFE). A decision tree structure is used to implement the decision feedback equalizer, in which each leaf of the tree is a type-2 FAF. This DFE vastly reduces computational complexity as compared to a TE. Simulation results show that equalizers based on type-2 FAFs perform much better than nearest neighbor classifiers (NNC) or equalizers based on type-1 FAFs

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:8 ,  Issue: 5 )