  In this paper we propose a new efficient message passing algorithm for
decoding LDPC transmitted over a channel with strong phase noise. The algorithm
performs approximate bayesian inference on a factor graph representation of the
channel and code joint posterior. The approximate inference is based on an
improved canonical model for the messages of the Sum & Product Algorithm, and a
method for clustering the messages using the directional statistics framework.
The proposed canonical model includes treatment for phase slips which can limit
the performance of tracking algorithms. We show simulation results and
complexity analysis for the proposed algorithm demonstrating its superiority
over some of the current state of the art algorithms.
