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

arXiv: https://arxiv.org/abs/2211.11406 · HTML: https://mlsanity.com/paper/2211.11406/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery](https://mlsanity.com/paper/2103.02582/) (2021)
- [Learning to Decode Linear Codes Using Deep Learning](https://mlsanity.com/paper/1607.04793/) (2016)
- [Model-Driven Deep Learning for Physical Layer Communications](https://mlsanity.com/paper/1809.06059/) (2018)
- [Differentiable Factor Graph Optimization for Learning Smoothers](https://mlsanity.com/paper/2105.08257/) (2021)
- [Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation](https://mlsanity.com/paper/1901.08621/) (2019)
- [Learned Factor Graphs for Inference from Stationary Time Sequences](https://mlsanity.com/paper/2006.03258/) (2020)
- [Low-complexity Near-optimum Symbol Detection Based on Neural Enhancement of Factor Graphs](https://mlsanity.com/paper/2203.16417/) (2022)
- [Fixed Points of Belief Propagation -- An Analysis via Polynomial Homotopy Continuation](https://mlsanity.com/paper/1605.06451/) (2016)
- [Neural Enhancement of Factor Graph-based Symbol Detection](https://mlsanity.com/paper/2203.03333/) (2022)
- [Ground Encoding: Learned Factor Graph-based Models for Localizing Ground Penetrating Radar](https://mlsanity.com/paper/2103.15317/) (2021)

## Built on by (most cited first, 1 of 1)

- [Local Message Passing on Frustrated Systems](https://mlsanity.com/paper/2306.01494/) (2023)

## Abstract (arXiv)

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.