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 or the same decoder multiple times with some redundancy, while the latter family utilizes emerging deep learning schemes to learn to decode from data. In this work we introduce a novel polar decoder that combines the list-decoding with neural-decoding, by forming an ensemble of multiple weighted belief-propagation (WBP) decoders, each trained to decode different data. We employ the cyclic-redundancy check (CRC) 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 scheme over a wide range of polar codes lengths, empirically showing that gains of around 0.25dB in frame-error rate could be achieved. Moreover, we provide complexity and latency analysis, showing that the number of operations required approaches that of a single BP decoder at high signal-to-noise ratios.

翻译：极化码具有优异的纠错能力。然而，极化码的译码通常具有挑战性，尤其是对于大码块而言，最近提出的译码器主要基于列表译码或神经译码。前者应用多个译码器或对同一译码器多次运行并引入一定冗余，而后者利用新兴的深度学习方案从数据中学习译码。本文提出一种新颖的极化码译码器，通过构建多个加权置信传播（WBP）译码器的集成，将列表译码与神经译码相结合，每个译码器针对不同的数据进行训练。我们采用循环冗余校验（CRC）码作为代理，用于集成译码器的组合与推理后最可能译码码字的选择，同时实现实时译码。我们在多种极化码长度下评估了该方案，实验表明在误帧率上可实现约0.25dB的增益。此外，我们提供了复杂度与延迟分析，表明在高信噪比下所需操作数量接近单个BP译码器。