LDPC codes: tracking non-stationary channel noise using sequential variational Bayesian estimates
This addresses the problem of improving communication reliability in 5G systems by enabling better noise tracking, though it appears incremental as it builds on existing LDPC and Bayesian methods.
The paper tackled the problem of tracking non-stationary channel noise in LDPC codes by developing a sequential Bayesian learning method, and the result showed that it outperformed an LDPC code with fixed noise knowledge on real-world 5G drive test data.
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.