Symbol Detection with Time-varying Unknown Phase by Expectation Propagation
Tao Wei, Yufei Huang, Yuan Qi · 2006
In digital communications, symbol detection in phase noise is an important topic that has been discussed in many papers under different conditions. In this paper, we consider symbol detection with time-varying unknown phase. We propose a solution based on expectation propagation (EP). EP is an extension to belief propagation and developed in machine learning. We point out that the developed EP solution can be considered as an iterated extended Kalman smoother (EKS). However, a crucial step of recycling the likelihoods in EP makes possible the further improvement over EKS. We show in the simulation that EP can produce very good performance with relatively low complexity. Since it produce soft information, the EP solution can be readily applied to iterative detection of coded systems.