Minimax mutual information approach for independent component analysis
Neural Computation
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Stochastic blind equalization based on PDF fitting using Parzen estimator
IEEE Transactions on Signal Processing
RF interference suppression for VDSL system
IEEE Transactions on Consumer Electronics
A 1.6 Mbps digital-QAM system for DSL transmission
IEEE Journal on Selected Areas in Communications
Fast fixed-point neural blind-deconvolution algorithm
IEEE Transactions on Neural Networks
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In this paper we propose a new closed approximated formed expression for the conditional expectation based on the Maximum Entropy principle and Laplace integral method. Our proposed expression does not impose any restrictions (except of even symmetric) on the probability distribution of the (unobserved) input sequence, thus it is suitable for a wider range of source probability density function compared with Bellini's, Fiori's or Haykin's expression. In addition, we propose a new, efficient and noniterative approximation for the Lagrange multipliers related to the blind deconvolution problem. Our proposed Lagrange multipliers are based on our new proposed expression for the conditional expectation and on the mean square error (MSE) criteria. Based on our new derivations, a new blind deconvolution algorithm is proposed with improved equalization performance compared with Godard's, reduced constellation algorithm (RCA), Fiori's and the sign reduced constellation algorithm (SRCA). In addition, a theoretical analysis shows that our algorithm achieves perfect equalization in the real valued and two independent quadrature carrier case. These results imply a significant improvement over Godard's algorithm in the 16 quadrature amplitude modulation (QAM) case.