## Abstract

We formulate the adaptive channel equalization as a conditional probability distribution learning problem. Conditional probability density function of the transmitted signal given the received signal is parametrized by a sigmoidal perceptron. In this framework, we use relative entropy (Kullback-Leibler distance) between the true and the estimated distributions as the cost function to be minimized. The true probabilities are approximated by their stochastic estimators resulting in a stochastic relative entropy cost function. This function is well-formed in the sense of Wittner and Denker, therefore gradient descent on this cost function is guaranteed to find a solution. The consistency and asymptotic normality of this learning scheme are shown via Maximum Partial Likelihood estimation of logistic models. As a practical example, we demonstrate that the resulting algorithm successfully equalizes multipath channels.

Original language | English (US) |
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Article number | 390039 |

Pages (from-to) | III297-III300 |

Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |

Volume | 3 |

DOIs | |

State | Published - 1994 |

Externally published | Yes |

Event | Proceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6) - Adelaide, Aust Duration: Apr 19 1994 → Apr 22 1994 |

## ASJC Scopus subject areas

- Software
- Signal Processing
- Electrical and Electronic Engineering