CRC-Aided Belief Propagation List Decoding of Polar Codes
CRC-Aided Belief Propagation List Decoding of Polar Codes
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DOI：
10.1109/isit44484.2020.9174249
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
Marvin Geiselhart;Ahmed Elkelesh;Moustafa Ebada;Sebastian Cammerer;S. Brink
中科院分区：
文献类型：
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作者：
Marvin Geiselhart;Ahmed Elkelesh;Moustafa Ebada;Sebastian Cammerer;S. Brink
尽管极化码的迭代解码最近基于置换因子图的思想取得了巨大进展，但与现有技术的CRC辅助连续消除列表（CA-SCL）解码相比，它仍然遭受不可忽略的性能降级。在这项工作中，我们证明了基于置信传播列表（BPL）算法的极化码迭代解码可以接近CA-SCL解码的错误率性能，因此可以有效地用于解码标准化的5G极化码。而不是仅利用循环冗余校验（CRC）作为停止条件（即，对于错误检测），我们还旨在受益于外部CRC码的纠错能力。为此，我们开发了两种不同的软判决CRC解码算法：基于Bahl-Cocke-Jelinek-Raviv（BCJR）的方法和基于和积算法（SPA）的方法。此外，置换因子图的优化选择进行了分析，并显示，以降低解码的复杂性显着。最后，我们将所提出的CRC辅助置信传播列表（CA-BPL）解码与CA-SCL解码下的最先进的5G极化码进行基准测试，从而展示了不仅接近CA-SCL而且接近有序统计解码（OSD）估计的最大似然（ML）界的错误率性能。
Although iterative decoding of polar codes has recently made huge progress based on the idea of permuted factor graphs, it still suffers from a non-negligible performance degradation when compared to state-of-the-art CRC-aided successive cancellation list (CA-SCL) decoding. In this work, we show that iterative decoding of polar codes based on the belief propagation list (BPL) algorithm can approach the error-rate performance of CA-SCL decoding and, thus, can be efficiently used for decoding the standardized 5G polar codes. Rather than only utilizing the cyclic redundancy check (CRC) as a stopping condition (i.e., for error-detection), we also aim to benefit from the error-correction capabilities of the outer CRC code. For this, we develop two distinct soft-decision CRC decoding algorithms: a Bahl-Cocke-Jelinek-Raviv (BCJR)-based approach and a sum product algorithm (SPA)-based approach. Further, an optimized selection of permuted factor graphs is analyzed and shown to reduce the decoding complexity significantly. Finally, we benchmark the proposed CRC-aided belief propagation list (CA-BPL) decoding to state-of-the-art 5G polar codes under CA-SCL decoding and, thereby, showcase an error-rate performance not just close to the CA-SCL but also close to the maximum likelihood (ML) bound as estimated by ordered statistic decoding (OSD).