Phase Noise Detection via Expectation Propagation and Related Algorithms

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.

Detection for Flat-Fading Channels based on Expectation Propagation

This paper aims at tackling the problem of signal detection in flat-fading channels. In this context, receivers based on the expectation propagation framework appear to be very promising although presenting some critical issues. We develop a new algorithm based on this framework where, unlike previous works, convergence is achieved after a single forward-backward pass, without additional inner detector iterations. The proposed message scheduling, together with novel adjustments of the approximating distributions' parameters, allows to obtain significant performance advantages with respect to the state-of-the-art solution. Simulation results show the applicability of this algorithm when sparser pilot configurations have to be adopted and a considerable gain compared to the current available strategies.