Gaussian Belief Propagation Based Multiuser Detection

TL;DR

This paper introduces a Gaussian Belief Propagation-based algorithm for multiuser detection that improves efficiency, reduces memory and computation, and offers new convergence insights, enhancing the MMSE detection process.

Contribution

It presents a novel, distributed Gaussian Belief Propagation approach for multiuser detection with improved performance and theoretical convergence guarantees.

Findings

Enhanced memory efficiency and reduced computation steps

Fewer messages exchanged in the detection process

Established convergence properties for the proposed algorithm

Abstract

In this work, we present a novel construction for solving the linear multiuser detection problem using the Gaussian Belief Propagation algorithm. Our algorithm yields an efficient, iterative and distributed implementation of the MMSE detector. We compare our algorithm's performance to a recent result and show an improved memory consumption, reduced computation steps and a reduction in the number of sent messages. We prove that recent work by Montanari et al. is an instance of our general algorithm, providing new convergence results for both algorithms.