CRC-AIDED LEARNED ENSEMBLES OF BELIEF-PROPAGATION POLAR DECODERS

Polar codes have promising error-correction capabilities. Yet, decoding polar codes is often challenging, particularly with large blocks, with recently proposed decoders based on list-decoding or neural-decoding. The former applies multiple decoders, while the latter family learns to decode from data. In this work we introduce a novel polar decoder that combines list-decoding with neural-decoding, by forming an ensemble of multiple weighted belief-propagation (BP) decoders trained with different data. We employ the cyclic-redundancy check code as a proxy for combining the ensemble decoders and selecting the most-likely decoded word after inference, while facilitating real-time decoding. We evaluate our decoder over a wide range of polar codes lengths, empirically showing gains of around 0.25dB in frame-error rate. Our complexity and latency analysis shows that the number of operations approaches that of a single BP decoder at high SNR.