LDPC codes: tracking non-stationary channel noise using sequential variational Bayesian estimates
Abstract
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.
Cite
@article{arxiv.2204.07037,
title = {LDPC codes: tracking non-stationary channel noise using sequential variational Bayesian estimates},
author = {J du Toit and J du Preez and R Wolhuter},
journal= {arXiv preprint arXiv:2204.07037},
year = {2023}
}
Comments
10 pages, 3 figures. arXiv admin note: text overlap with arXiv:2204.06350