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

Author

Listed:
  • J. Toit

    (Stellenbosch University)

  • J. Preez

    (Stellenbosch University)

  • R. Wolhuter

    (Stellenbosch University)

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.

Suggested Citation

  • J. Toit & J. Preez & R. Wolhuter, 2024. "Low-density parity-check codes: tracking non-stationary channel noise using sequential variational Bayesian estimates," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 85(2), pages 247-262, February.
    • Handle: RePEc:spr:telsys:v:85:y:2024:i:2:d:10.1007_s11235-023-01083-5
    DOI: 10.1007/s11235-023-01083-5
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