Simulation of communication systems
Simulation of communication systems
Recurrent radial basis function networks for optimal symbol-by-symbol equalization
Proceedings of the COST #229 international workshop on Adaptive methods and emergent techniques for signal processing and communications
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Soft-decision equalizers for in-service error rate monitoring
Signal Processing
The Practical Handbook of Genetic Algorithms: Applications, Second Edition
The Practical Handbook of Genetic Algorithms: Applications, Second Edition
Equalizers for Digital Modems
Wireless Communication
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Maximum likelihood joint channel and data estimation using geneticalgorithms
IEEE Transactions on Signal Processing
Genetic algorithm optimization for blind channel identificationwith higher order cumulant fitting
IEEE Transactions on Evolutionary Computation
Robust joint channel and noise estimation in Bayesian blind equalizers
Signal Processing
Journal of VLSI Signal Processing Systems
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The application of the Bayesian formulation to the joint data and channel estimation in digital communication is not feasible in practice because the computational complexity and memory requirements of the estimation process grow exponentially with time. However, the evolution with time of the channel conditional density model suggests the application of pruning, selection, crossover and other concepts from evolutionary computation and neural networks, which drastically reduce the complexity of the Bayesian equalizer without severe performance degradation. Although some problems of convergence to wrong channel estimates may arise, Bayesian equalizers can detect those situations by estimating, during operation, the overall symbol error probability. If suboptimal convergence is detected, the estimation process is automatically re-started.