Message-Passing Algorithms for Channel Estimation and Decoding Using Approximate Inference

TL;DR

This paper develops iterative receiver schemes for wireless communication by framing channel estimation and decoding as an inference problem, integrating belief propagation and mean field approximations for improved performance.

Contribution

It introduces a unified inference framework combining BP and MF, embedding expectation propagation and EM, and derives new message-passing receiver schemes with superior performance.

Findings

BP-MF based receiver achieves optimal performance-complexity trade-off

BP-EM variant offers enhanced numerical stability

Proposed schemes outperform existing algorithms in simulations

Abstract

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.