A family of nonlinear equalizers: Sub-optimal Bayesian classifiers
نویسندگان
چکیده
A family of sub-optimal Bayesian equalizers is proposed in two versions: feed-forward and decision feedback. We show that this family of equalizers provides a range of gradual choices concerning the tradeoff between equalizer complexity and symbol error rate (SER). We also point out the SER equivalence between the simplest proposed structure (the simplest equalizer of the family) and Wiener linear equalizer (or the decision feedback equalizer for the decision feedback version). Some simulations results are also presented.
منابع مشابه
Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters
This paper presents a new 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 ...
متن کاملThe Family of Scale-Mixture of Skew-Normal Distributions and Its Application in Bayesian Nonlinear Regression Models
In previous studies on fitting non-linear regression models with the symmetric structure the normality is usually assumed in the analysis of data. This choice may be inappropriate when the distribution of residual terms is asymmetric. Recently, the family of scale-mixture of skew-normal distributions is the main concern of many researchers. This family includes several skewed and heavy-tailed d...
متن کاملMaximum margin equalizers trained with the Adatron algorithm
In this paper we apply the structural risk minimization principle as an appropriate criterion to train decision feedback and transversal equalizers. We consider both linear discriminant (optimal hyperplane) and nonlinear discriminant (support vector machine) classi6ers as an alternative to the linear minimum mean-square error (MMSE) equalizer and radial basis function (RBF) networks, respective...
متن کاملDecision boundary for discrete Bayesian network classifiers
Bayesian network classifiers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classifiers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V -structures in the predictor sub-graph, we a...
متن کاملAdaptive Fuzzy Neural Filtering for Decision Feedback Equalization and Multi-Antenna Systems
1.1 Background In ordinary channel equalizer and multi-antenna system, many types of detecting methods have been proposed to compensate the distorted signals or recover the original symbols of the desired user [1]-[3]. For channel equalization, transversal equalizers (TEs) and decision feedback equalizers (DFEs) are commonly used as a detector to compensate the distorted signals [2]. It is well...
متن کامل