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.)
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Soft-decision equalizers for in-service error rate monitoring
Signal Processing
Equalizers for Digital Modems
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
A novel cluster based MLSE equalizer for M-PAM signaling schemes
Signal Processing
Maximum likelihood joint channel and data estimation using geneticalgorithms
IEEE Transactions on Signal Processing
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Genetic algorithm optimization for blind channel identificationwith higher order cumulant fitting
IEEE Transactions on Evolutionary Computation
Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering
IEEE Transactions on Information Theory
Journal of VLSI Signal Processing Systems
Robust deconvolution for ARMAX models with Gaussian uncertainties
Signal Processing
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In this paper, we discuss the problem of reducing the complexity of Bayesian approaches to the joint channel and data detection issues in digital communications. Taking ideas from Bayesian Analysis and Evolutionary Computation, we propose and compare different equalization schemes whose complexity is kept moderate by applying pruning and merging operations over an otherwise growing population of channel estimates. Already existing deterministic methods for complexity limitation as well as new stochastic ones are analyzed and compared. Using the capability of Bayesian equalizers to estimate the symbol error probability during operation, a convergence control mechanism is introduced. The joint channel and noise Bayesian estimation is shown to offer more robust BER estimates.