  In the context of signal detection in the presence of an unknown time-varying
channel parameter, receivers based on the Expectation Propagation (EP)
framework appear to be very promising. EP is a message-passing algorithm based
on factor graphs with an inherent ability to combine prior knowledge of system
variables with channel observations. This suggests that an effective estimation
of random channel parameters can be achieved even with a very limited number of
pilot symbols, thus increasing the payload efficiency. However, achieving
satisfactory performance often requires ad-hoc adjustments in the way the
probability distributions of latent variables - both data and channel
parameters - are combined and projected. Here, we apply EP to a classical
problem of coded transmission on a strong Wiener phase noise channel, employing
soft-input soft-output decoding. We identify its limitations and propose new
strategies which reach the performance benchmark while maintaining low
complexity, with a primary focus on challenging scenarios where the
state-of-the-art algorithms fail.
