Message-Passing Algorithms for Channel Estimation and Decoding Using   Approximate Inference
  We design iterative receiver schemes for a generic wireless communication system by treating channel estimation and information decoding as an inference problem in graphical models. We introduce a recently proposed inference framework that combines belief propagation (BP) and the mean field (MF) approximation and includes these algorithms as special cases. We also show that the expectation propagation and expectation maximization algorithms can be embedded in the BP-MF framework with slight modifications. By applying the considered inference algorithms to our probabilistic model, we derive four different message-passing receiver schemes. Our numerical evaluation demonstrates that the receiver based on the BP-MF framework and its variant based on BP-EM yield the best compromise between performance, computational complexity and numerical stability among all candidate algorithms. 
Mihai-Alin Badiu, Gunvor Elisabeth Kirkelund, Carles Navarro   Manch\'on, Erwin Riegler, Bernard Henri Fleury
cs.IT math.IT stat.ML