Blind and semi-blind equalization using hidden Markov models and clustering techniques

Estimation of stochastic signals under partially missing information

“…The set of associated observed signals y ω ðt 1 Þ; …; y ω ðt p Þ is denoted by S ðpÞ y . Then for all y ω ðtÞ that do not belong to S ðpÞ y , the filter F ðp À 1Þ is said to be a blind filter [17,18] since no information on x ω ðtÞ = 2 S ðpÞ x is available. If y ω ðtÞ A S ðpÞ y then F ðp À 1Þ becomes a nonblind filter since information on x ω ðtÞ A S ðpÞ x is available.…”

Algoritmo Híbrido Para Estimação ML De Modelos HMM Baseado Em Treinamento De Viterbi E Otimização PSO

“…Os Modelos de Markov Escondidos (HMM, de Hidden Markov Model) têm sido aplicados em diversasáreas de pesquisa, tais como reconhecimento de locutor [1], processamento de imagem e visão computacional [2], reconhecimento de texto [3], canais de comunicações com erros em surtos [4], entre outros [5][6][7][8].…”

Performance Comparison of Several Non-Linear Equalizers in the Context of Mobile Telecommunications

“…The third approach is based on clustering techniques for defining training algorithms, which are analysed in the context of classification theory. In this case the equalizer may be either a simple classifier (Georgoulakis and Theodoridis, 2000) or a radial-basis function network (RBF) (Chen, Mulgrew, and Grant, 1993;Chen, McLaughlin, and Mulgrew, 1994;Gan et al, 1999). Finally, the fourth approach employs hybrid structures, such as the combinations of the self-organized map (SOM) along with the the RBF network , as well as the combinations involving linear transversal filtering (LTE) and the SOM , also including LTE-RBF and LTE-MLP Castanié, 1998/1999).…”