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

J. Toit (), J. Preez () and R. Wolhuter ()
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J. Toit: Stellenbosch University
J. Preez: Stellenbosch University
R. Wolhuter: Stellenbosch University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2024, vol. 85, issue 2, No 5, 247-262

Abstract: Abstract 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.

Keywords: LDPC codes; Bayesian sequential learning; Variational inference; Cluster graphs (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11235-023-01083-5

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