Window-based expectation propagation for adaptive signal detection in flat-fading channels

In this paper, we propose a new Bayesian receiver for signal detection in flat-fading channels. First, the detection problem is formulated as an inference problem in a graphical model that models a hybrid dynamic system with both continuous and discrete variables. Then, based on the expectation propagation (EP) framework, we develop a smoothing algorithm to address the inference problem and visualize this algorithm using factor graphs. As a generalization of loopy belief propagation, EP efficiently approximates Bayesian estimation by iteratively propagating information between different nodes in the graphical model and projecting the posterior distributions into the exponential family. We use window-based EP smoothing for online estimation as in the signal detection problem. Window-based EP smoothing achieves accuracy similar to that obtained by batch EP smoothing, as shown in our simulations, while reducing delay time. Compared to sequential Monte Carlo filters and smoothers, the new method has lower computational complexity since it makes analytically deterministic approximation instead of Monte Carlo approximations. Our simulations demonstrate that the new receiver achieves accurate detection without the aid of any training symbols or decision feedbacks. Furthermore, the new receiver achieves accuracy comparable to that achieved by sequential Monte Carlo methods, but with less than one-tenth computational cost.