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arXiv Machine Learning By Felix Krieg, Paul Bezner, Stephan ten Brink

Joint End-to-End Training of Weighted BP Decoder Ensembles

Read the original on arXiv Machine Learning →

The paper presents a method for jointly training an ensemble of weighted belief‑propagation decoders by optimizing trainable Tanner‑graph edge weights. Four ensemble objectives are compared, and best‑member training is found to be most effective. On a (64,32) PEG LDPC code, the ensemble achieves a 0.66 dB gain over a single decoder at FER = 10⁻⁴, and similar improvements are seen on a (128,64) CCSDS LDPC code, demonstrating learned complementary failure events rather than uniformly better components.

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