Sequence estimation and channel equalization using forward decoding kernel machines

A forward decoding approach to kernel machine learning is presented. The method combines concepts from Markovian dynamics, large margin classifiers and reproducing kernels for robust sequence detection by learning inter-data dependencies. A MAP (maximum a posteriori) sequence estimator is obtained by regressing transition probabilities between symbols as a function of received data. The training procedure involves maximizing a lower bound of a regularized cross-entropy on the posterior probabilities, which simplifies into direct estimation of transition probabilities using kernel logistic regression. Applied to channel equalization, forward decoding kernel machines outperform support vector machines and other techniques by about 5dB in SNR for given BER, within 1 dB of theoretical limits.