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Quantized Iterative Message Passing Decoders with Low Error Floor for LDPC Codes

机译：低错误本底的LDPC码量化迭代消息传递解码器

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摘要

The error floor phenomenon observed with LDPC codes and their graph-based, iterative, message-passing (MP) decoders is commonly attributed to the existence of error-prone substructures — variously referred to as near-codewords, trapping sets, absorbing sets, or pseudocodewords — in a Tanner graph representation of the code. Many approaches have been proposed to lower the error floor by designing new LDPC codes with fewer such substructures or by modifying the decoding algorithm. Using a theoretical analysis of iterative MP decoding in an idealized trapping set scenario, we show that a contributor to the error floors observed in the literature may be the imprecise implementation of decoding algorithms and, in particular, the message quantization rules used. We then propose a new quantization method — (q+1)-bit quasi-uniform quantization — that efficiently increases the dynamic range of messages, thereby overcoming a limitation of conventional quantization schemes. Finally, we use the quasi-uniform quantizer to decode several LDPC codes that suffer from high error floors with traditional fixed-point decoder implementations. The performance simulation results provide evidence that the proposed quantization scheme can, for a wide variety of codes, significantly lower error floors with minimal increase in decoder complexity.
机译：使用LDPC码及其基于图的迭代消息传递（MP）解码器观察到的错误基底现象通常归因于容易出错的子结构的存在，这些子结构被称为近码字，陷阱集，吸收集或伪代码字-代码的Tanner图表示形式。已经提出了许多方法来通过设计具有较少这种子结构的新的LDPC码或通过修改解码算法来降低错误率。使用理想化的陷印集方案中的迭代MP解码的理论分析，我们表明，文献中观察到的错误底线的一个可能是解码算法的不精确实现，尤其是所使用的消息量化规则。然后，我们提出了一种新的量化方法-（q + 1）位准均匀量化-有效地增加了消息的动态范围，从而克服了传统量化方案的局限性。最终，我们使用准均匀量化器对传统定点解码器实现中受高误码率影响的几个LDPC码进行解码。性能仿真结果提供了证据，表明所提出的量化方案可以针对各种代码在降低解码器复杂性的情况下显着降低误码率。

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