PROJECT TITLE :

Analog Digital Belief Propagation

ABSTRACT:

We tend to introduce a message passing belief propagation (BP) algorithm for factor graph over linear models that uses messages in the form of Gaussian-like distributions. With respect to the regular Gaussian BP, the proposed algorithm adds 2 operations to the model, namely the wrapping and the discretization of variables. This addition needs the derivation of correct modifications of message representations and updating rules at the BP nodes. We named the new algorithm Analog-Digital-Belief-Propagation (ADBP). The ADBP permits to construct iterative decoders for mod-M ring encoders that have a complexity freelance from the scale M of the alphabets, therefore yielding economical decoders for very high spectral efficiencies.


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