Stochastic maximum likelihood methods for semi-blind channel equalization

Hakan A. Cirpan*, Michail K. Tsatsanis

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


In this paper, a blind stochastic maximum likelihood channel equalization algorithm is adapted to incorporate a known training sequence as part of the transmitted frame. A Hidden Markov Model formulation of the problem is introduced and the Baum-Welch algorithm is modified to provide a computationally efficient solution to the resulting optimization problem. The proposed method provides a unified framework for semi-blind channel estimation, which exploits information from both the training and the blind part of the received data record. The performance of the maximum likelihood estimator is studied, based on the evaluation of Cramer-Rao bounds. Finally, some simulation results are presented.

Original languageEnglish
Pages (from-to)1629-1632
Number of pages4
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
Publication statusPublished - 1998
Externally publishedYes
EventProceedings of the 1997 31st Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) - Pacific Grove, CA, USA
Duration: 2 Nov 19975 Nov 1997


Dive into the research topics of 'Stochastic maximum likelihood methods for semi-blind channel equalization'. Together they form a unique fingerprint.

Cite this