Structural Optimization of Factor Graphs for Symbol Detection via Continuous Clustering and Machine Learning

TL;DR

This paper introduces a machine learning-based method to optimize factor graph structures for symbol detection in channels with inter-symbol interference, achieving near-optimal detection performance.

Contribution

It presents a novel end-to-end structural optimization approach for factor graphs using clustering and neural belief propagation, improving detection accuracy.

Findings

Optimized factor graph structures enhance detection performance.

Clustering-based structural optimization outperforms traditional methods.

Near-maximum a posteriori detection achieved for specific channels.

Abstract

We propose a novel method to optimize the structure of factor graphs for graph-based inference. As an example inference task, we consider symbol detection on linear inter-symbol interference channels. The factor graph framework has the potential to yield low-complexity symbol detectors. However, the sum-product algorithm on cyclic factor graphs is suboptimal and its performance is highly sensitive to the underlying graph. Therefore, we optimize the structure of the underlying factor graphs in an end-to-end manner using machine learning. For that purpose, we transform the structural optimization into a clustering problem of low-degree factor nodes that incorporates the known channel model into the optimization. Furthermore, we study the combination of this approach with neural belief propagation, yielding near-maximum a posteriori symbol detection performance for specific channels.