Active Deep Decoding of Linear Codes
This work addresses error decoding in communication systems by enhancing deep learning models with domain-specific knowledge, though it is incremental as it builds on existing WBP methods.
The paper tackled the problem of improving Weighted Belief Propagation (WBP) decoding for linear codes by introducing two novel methods that leverage active learning to sample data efficiently, resulting in up to 0.4dB improvement in the waterfall region and up to 1.5dB in the errorfloor region for BCH codes without increasing inference complexity.
High quality data is essential in deep learning to train a robust model. While in other fields data is sparse and costly to collect, in error decoding it is free to query and label thus allowing potential data exploitation. Utilizing this fact and inspired by active learning, two novel methods are introduced to improve Weighted Belief Propagation (WBP) decoding. These methods incorporate machine-learning concepts with error decoding measures. For BCH(63,36), (63,45) and (127,64) codes, with cycle-reduced parity-check matrices, improvement of up to 0.4dB at the waterfall region, and of up to 1.5dB at the errorfloor region in FER, over the original WBP, is demonstrated by smartly sampling the data, without increasing inference (decoding) complexity. The proposed methods constitutes an example guidelines for model enhancement by incorporation of domain knowledge from error-correcting field into a deep learning model. These guidelines can be adapted to any other deep learning based communication block.