Low-density parity-check codes: tracking non-stationary channel noise using sequential variational Bayesian estimates.

Autor:	du Toit, J., du Preez, J., Wolhuter, R.
Předmět:	SEQUENTIAL learning SIGNAL-to-noise ratio GRAPH algorithms LOW density parity check codes TANNER graphs ARTIFICIAL satellite tracking
Zdroj:	Telecommunication Systems; Feb2024, Vol. 85 Issue 2, p247-262, 16p
Abstrakt:	We present a sequential Bayesian learning method for tracking non-stationary signal-to-noise ratios in low-density parity-check (LDPC) codes by way of 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 can track non-stationary channel noise accurately while adding performance benefits to the LDPC code, which outperforms an LDPC code with a fixed stationary knowledge of the actual channel noise. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Full text from SpringerLink