# Low-complexity Near-optimum Symbol Detection Based on Neural Enhancement of Factor Graphs

arXiv: https://arxiv.org/abs/2203.16417 · HTML: https://mlsanity.com/paper/2203.16417/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [Deep Learning Methods for Improved Decoding of Linear Codes](https://mlsanity.com/paper/1706.07043/) (2017)
- [Learning to Decode Linear Codes Using Deep Learning](https://mlsanity.com/paper/1607.04793/) (2016)
- [Neural Enhanced Belief Propagation on Factor Graphs](https://mlsanity.com/paper/2003.01998/) (2020)
- [Data-Driven Factor Graphs for Deep Symbol Detection](https://mlsanity.com/paper/2002.00758/) (2020)
- [Achievable Information Rates for Fiber Optics: Applications and Computations](https://mlsanity.com/paper/1709.10393/) (2017)
- [Neural Enhancement of Factor Graph-based Symbol Detection](https://mlsanity.com/paper/2203.03333/) (2022)

## Built on by (most cited first, 8 of 8)

- [Graph Neural Networks for Channel Decoding](https://mlsanity.com/paper/2207.14742/) (2022)
- [Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs](https://mlsanity.com/paper/2401.12627/) (2024)
- [Structural Optimization of Factor Graphs for Symbol Detection via Continuous Clustering and Machine Learning](https://mlsanity.com/paper/2211.11406/) (2022)
- [Deep Learning-Based Detection for Marker Codes over Insertion and Deletion Channels](https://mlsanity.com/paper/2401.01155/) (2024)
- [Invariant Transformation and Resampling based Epistemic-Uncertainty Reduction](https://mlsanity.com/paper/2602.23315/) (2026)
- [Dual-Attention Based 3D Channel Estimation](https://mlsanity.com/paper/2604.01769/) (2026)
- [Local Message Passing on Frustrated Systems](https://mlsanity.com/paper/2306.01494/) (2023)
- [Optimization of Iterative Blind Detection based on Expectation Maximization and Belief Propagation](https://mlsanity.com/paper/2408.02312/) (2024)

## Abstract (arXiv)

We consider the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Based on the Ungerboeck observation model, a detection algorithm with appealing complexity properties can be derived. However, since the underlying factor graph contains cycles, the sum-product algorithm (SPA) yields a suboptimal algorithm. In this paper, we develop and evaluate efficient strategies to improve the performance of the factor graph-based symbol detection by means of neural enhancement. In particular, we consider neural belief propagation and generalizations of the factor nodes as an effective way to mitigate the effect of cycles within the factor graph. By applying a generic preprocessor to the channel output, we propose a simple technique to vary the underlying factor graph in every SPA iteration. Using this dynamic factor graph transition, we intend to preserve the extrinsic nature of the SPA messages which is otherwise impaired due to cycles. Simulation results show that the proposed methods can massively improve the detection performance, even approaching the maximum a posteriori performance for various transmission scenarios, while preserving a complexity which is linear in both the block length and the channel memory.