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Polar codes have promising error-correction capabilities. Yet, decoding polarcodes is often challenging, particularly with large blocks, with recentlyproposed decoders based on list-decoding or neural-decoding. The former appliesmultiple decoders or the same decoder multiple times with some redundancy,while the latter family utilizes emerging deep learning schemes to learn todecode from data. In this work we introduce a novel polar decoder that combinesthe list-decoding with neural-decoding, by forming an ensemble of multipleweighted belief-propagation (WBP) decoders, each trained to decode differentdata. We employ the cyclic-redundancy check (CRC) code as a proxy for combiningthe ensemble decoders and selecting the most-likely decoded word afterinference, while facilitating real-time decoding. We evaluate our scheme over awide range of polar codes lengths, empirically showing that gains of around0.25dB in frame-error rate could be achieved. Moreover, we provide complexityand latency analysis, showing that the number of operations required approachesthat of a single BP decoder at high signal-to-noise ratios.

CRC-Aided Learned Ensembles of Belief-Propagation Polar Decoders