# LDPC codes: tracking non-stationary channel noise using sequential variational Bayesian estimates

arXiv: https://arxiv.org/abs/2204.07037 · HTML: https://mlsanity.com/paper/2204.07037/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [Graph Coloring: Comparing Cluster Graphs to Factor Graphs](https://mlsanity.com/paper/2110.02048/) (2021)
- [Probabilistic Receiver Architecture Combining BP, MF, and EP for Multi-Signal Detection](https://mlsanity.com/paper/1604.04834/) (2016)

## Abstract (arXiv)

We present a sequential Bayesian learning method for tracking non-stationary signal-to-noise ratios in LDPC codes using probabilistic graphical models. We represent the LDPC code as a cluster graph using a general purpose cluster graph construction algorithm called the layered trees running intersection property (LTRIP) algorithm. The channel noise estimator is a global Gamma cluster, which we extend to allow for Bayesian tracking of non-stationary noise variation. We evaluate our proposed model on real-world 5G drive test data. Our results show that our model is capable of tracking non-stationary channel noise, which outperforms an LDPC code with a fixed knowledge of the actual average channel noise.