A stochastic unbiased minimum mean error rate algorithm for decision feedback equalizers
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Adaptive minimum error-rate filtering design: A review
Signal Processing
Minimum symbol error rate carrier phase recovery of QPSK
IEEE Transactions on Signal Processing
Minimum bayes risk adaptive linear equalizers
IEEE Transactions on Signal Processing
Error recovery of variable length code over BSC with arbitrary crossover probability
IEEE Transactions on Communications
Least-symbol-error-rate adaptive decision feedback equalization for underwater channel
Proceedings of the Eighth ACM International Conference on Underwater Networks and Systems
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The design of decision feedback equalizers (DFEs) is typically based on the minimum mean square error (MMSE) principle as this leads to effective adaptive implementation in the form of the least mean square algorithm. It is well-known, however, that in certain situations, the MMSE solution can be distinctly inferior to the optimal minimum symbol error rate (MSER) solution. We consider the MSER design for multilevel pulse-amplitude modulation. Block-data adaptive implementation of the theoretical MSER DFE solution is developed based on the Parzen window estimate of a probability density function. Furthermore, a sample-by-sample adaptive MSER algorithm, called the least symbol error rate (LSER), is derived for adaptive equalization applications. The proposed LSER algorithm has a complexity that increases linearly with the equalizer length. Computer simulation is employed to evaluate the proposed alternative MSER design for equalization application with multilevel signaling schemes.