A Message-Passing Approach for Joint Channel Estimation, Interference Mitigation and Decoding

TL;DR

This paper introduces a message-passing receiver architecture that jointly estimates channels, mitigates interference, and decodes signals in wireless systems with time-varying fading and non-Gaussian interference, outperforming traditional methods.

Contribution

It proposes a novel iterative message-passing approach that exploits interference statistics and correlated fading, with limited Gaussian mixture components for manageable complexity.

Findings

Significantly improved performance over conventional receivers.

Robustness to fading model mismatches.

Constant complexity per symbol regardless of frame length.

Abstract

Channel uncertainty and co-channel interference are two major challenges in the design of wireless systems such as future generation cellular networks. This paper studies receiver design for a wireless channel model with both time-varying Rayleigh fading and strong co-channel interference of similar form as the desired signal. It is assumed that the channel coefficients of the desired signal can be estimated through the use of pilots, whereas no pilot for the interference signal is available, as is the case in many practical wireless systems. Because the interference process is non-Gaussian, treating it as Gaussian noise generally often leads to unacceptable performance. In order to exploit the statistics of the interference and correlated fading in time, an iterative message-passing architecture is proposed for joint channel estimation, interference mitigation and decoding. Each message takes the form of a mixture of Gaussian densities where the number of components is limited so that the overall complexity of the receiver is constant per symbol regardless of the frame and code lengths. Simulation of both coded and uncoded systems shows that the receiver performs significantly better than conventional receivers with linear channel estimation, and is robust with respect to mismatch in the assumed fading model.