@inproceedings{ec91d293af66478cafe4c0770d7e1f57,
title = "A Log-Likelihood Ratio based Generalized Belief Propagation",
abstract = "In this paper, we propose a reduced complexity Generalized Belief Propagation (GBP) that propagates messages in Log-Likelihood Ratio (LLR) domain. The key novelties of the proposed LLR-GBP are: (i) reduced fixed point precision for messages instead of computational complex floating point format, (ii) operations performed in logarithm domain, thus eliminating the need for multiplications and divisions, (iii) usage of message ratios that leads to simple hard decision mechanisms. We demonstrated the validity of LLR-GBP on reconstruction of images passed through binary-input two-dimensional Gaussian channels with memory and affected by additive white Gaussian noise.",
keywords = "Probabilistic inference, generalized belief propagation (GBP), graphical models",
author = "Alexandru Amaricai and Mohsem Bahrami and Bane Vasi{\'c}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 18th International Conference on Smart Technologies, EUROCON 2019 ; Conference date: 01-07-2019 Through 04-07-2019",
year = "2019",
month = jul,
doi = "10.1109/EUROCON.2019.8861528",
language = "English (US)",
series = "EUROCON 2019 - 18th International Conference on Smart Technologies",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Boris Dumnic and Marko Delimar and Cedomir Stefanovic",
booktitle = "EUROCON 2019 - 18th International Conference on Smart Technologies",
}