Improving error correction by training diverse decoder ensembles jointly
Joint End-to-End Training of Weighted BP Decoder Ensembles
Information TheoryMachine Learning
Summary
When digital messages are sent, they can get mixed up or corrupted. To fix this, devices use decoders that try to correct these errors. The authors found a new way to train a group of decoders together so they make different kinds of mistakes, which means when combined, they fix errors better than any single decoder. This teamwork approach improved decoding for certain types of codes commonly used in communication. Interestingly, each decoder alone got worse, but together they worked much better.
What this means in practice
- •For communication engineers: Create more reliable decoders for digital communication systems by jointly training multiple decoders to specialize and complement each other.
- •For satellite system designers: Improve error correction in satellite communications using trained decoder ensembles tailored for CCSDS LDPC codes.
Authors
Felix Krieg, Paul Bezner, Stephan ten Brink
Abstract
Ensemble decoding improves decoding reliability by combining component decoders with complementary failure events. Existing approaches typically obtain this diversity from code-specific structure, such as code automorphisms, multiple parity-check representations, or targeted modifications of the message-passing graph. In this work, we instead learn the diversity of an ensemble of weighted belief propagation decoders by jointly optimizing trainable Tanner-graph edge weights. A central design question is how the training objective should distribute gradient among the ensemble members to promote complementary decoding behavior. To this end, we compare four ensemble objectives under direct gradient-based optimization and identify best-member training as the most effective approach. For a (64,32) progressive edge-growth (PEG) low-density parity-check (LDPC) code, the resulting ensemble outperforms the considered trained and untrained belief-propagation-based ensemble baselines. With 10 members, it provides a gain of approximately 0.66 dB over a single belief propagation decoder at a target frame error rate (FER) of $10^{-4}$. Similar behavior is observed for a (128,64) CCSDS LDPC code, indicating that the learned complementarity is not specific to PEG. Although the ensemble performance improves substantially, all trained members show a higher standalone FER. The gain therefore results from learned specialization and complementary failure events rather than uniformly improved component decoders.