Statistical methods for blind equalization of communication channels.

Author(s)

Claudio José Bordin Júnior

• Document type: Doctoral Thesis
• Press: São Paulo.
• Institution: Universidade de São Paulo (USP). Escola Politécnica (EP/BC)
• Defense date: 2006-03-23

Examining board members

Luiz Antonio Baccalá; Marcelo Gomes da Silva Bruno; Oswaldo Luiz do Valle Costa; Vítor Heloiz Nascimento; Sérgio Wechsler

• Advisor: Luiz Antonio Baccalá

Abstract

In this thesis, we propose and analyze blind equalization methods suitable for linear FIR communications channels, focusing on the development of algorithms based on particle filters - recursive methods for approximating Bayesian solutions to stochastic filtering problems. Initially, we propose new equalization methods for signal models with gaussian additive noise that dispense with the need for differentially encoding the transmitted signals, as opposed to the previously existing methods. Next, we extend these algorithms to deal with non-gaussian additive noise by deploying artificial parameter evolution techniques. We next develop new joint blind equalization and decoding algorithms, suitable for convolutionally or block-coded communications systems. Via numerical simulations we show that the proposed algorithms outperform traditional approaches both in terms of mean bit error rate and convergence speed, and closely approach the performance of the optimal (MAP) trained equalizer. Furthermore, we observed that the methods based on deterministic particle filters consistently outperform those based on stochastic approaches, making them preferable when the adopted signal model allows for the analytic marginalization of the unknown channel parameters. (AU)