Linear neural network based blind equalization
Signal Processing
Fuzzy techniques for adaptive nonlinear equalization
Signal Processing - Special issue on fuzzy logic in signal processing
Blind Channel Equalization and Identification
Blind Channel Equalization and Identification
Hybrid simplex genetic algorithm for blind equalization using RBF networks
Mathematics and Computers in Simulation
A Constrained Optimisation Approach To The Blind Estimation Of Volterra Kernels
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Blind ZF equalization with controlled delay robust to order over estimation
Signal Processing - From signal processing theory to implementation
Modified fuzzy c-means and Bayesian equalizer for nonlinear blind channel
Applied Soft Computing
Adaptive Bayesian equalizer with decision feedback
IEEE Transactions on Signal Processing
Blind equalization of nonlinear channels from second-orderstatistics
IEEE Transactions on Signal Processing
Blind equalization of constant modulus signals using support vector machines
IEEE Transactions on Signal Processing
Blind equalization using higher order cumulants and neural network
IEEE Transactions on Signal Processing
Nonlinear channel blind equalization using hybrid genetic algorithm with simulated annealing
Mathematical and Computer Modelling: An International Journal
Adaptive Cancellation of Nonlinear Intersymbol Interference for Voiceband Data Transmission
IEEE Journal on Selected Areas in Communications
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A new hybrid blind equalization algorithm with steady-state performance analysis
Digital Signal Processing
| Hi-index | 0.00 |
In this study, the use of conditional Fuzzy C-Means (CFCM) aimed at estimation of desired states of an unknown digital communication channel is considered for blind channel equalization. In the proposed CFCM, a collection of estimated centers is treated as a set of pre-defined desired channel states, and used to extract channel output states. By considering the combinations of the extracted channel output states, all possible sets of desired channel states are constructed. The set of desired states characterized by the maximal value of the Bayesian fitness function is subsequently selected for the next clustering epoch. This modification of CFCM makes it possible to search for the optimal desired channel states of an unknown channel. Finally, given the desired channel states, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In a series of simulations, binary signals are generated at random with Gaussian noise, and both linear and nonlinear channels are evaluated. The experimental studies demonstrate that the performance (being expressed in terms of accuracy and speed) of the proposed CFCM is superior to the performance of the existing method exploiting the ''conventional'' Fuzzy C-Means (FCM).