论文标题
LDPC解码中的节点操作的重建计算量化方法（RCQ）方法
A Reconstruction-Computation-Quantization (RCQ) Approach to Node Operations in LDPC Decoding
论文作者
论文摘要
在本文中，我们提出了一种有限精确解码方法，该方法具有重建，计算和量化（RCQ）的三个步骤。与量化的信念传播（MIM-QBP）不同，RCQ可以近似信念传播或Min-sum解码。 MIM-QBP解码器面临的一个问题是，当学位2变量节点的比例很大时，它不能很好地工作。但是，如IEEE 802.11标准和DVB-S2标准所示，有时对于快速编码结构来说，必须大量的度量2可变节点。相比之下，提出的RCQ解码器可以应用于任何现成的LDPC代码，包括具有很大一部分度量2度可变节点的均可分析的代码。我们的模拟表明，4位Min-SUM-SUM RCQ解码器可为IEE EEE 802.11标准的LDP提供0.1DB的框架误差率（FER），可在0.1dB的0.1dB范围内进行0.1dB的范围，以下是IEE 802.11标准LD的范围。实际上，在高SNR区域中的表现优于完整的BP，因为它克服了基本的捕获集，该集合在BP解码下创建错误地板。本文还引入了分层动态量化（HDQ），以设计RCQ解码器所需的非均匀量化器。 HDQ是一种略有优化的低复杂性设计技术。对称二进制输入无内存的白色高斯噪声通道上HDQ和最佳量化的仿真结果显示，这两个量化器的相互信息的损失少于$ 10^{ - 6} $位，这对于实际应用而言是可忽略的。
In this paper, we propose a finite-precision decoding method that features the three steps of Reconstruction, Computation, and Quantization (RCQ). Unlike Mutual-Information-Maximization Quantized Belief Propagation (MIM-QBP), RCQ can approximate either belief propagation or Min-Sum decoding. One problem faced by MIM-QBP decoder is that it cannot work well when the fraction of degree-2 variable nodes is large. However, sometimes a large fraction of degree-2 variable nodes is necessary for a fast encoding structure, as seen in the IEEE 802.11 standard and the DVB-S2 standard. In contrast, the proposed RCQ decoder may be applied to any off-the-shelf LDPC code, including those with a large fraction of degree-2 variable nodes.Our simulations show that a 4-bit Min-Sum RCQ decoder delivers frame error rate (FER) performance around 0.1dB of full-precision belief propagation (BP) for the IEEE 802.11 standard LDPC code in the low SNR region.The RCQ decoder actually outperforms full-precision BP in the high SNR region because it overcomes elementary trapping sets that create an error floor under BP decoding. This paper also introduces Hierarchical Dynamic Quantization (HDQ) to design the non-uniform quantizers required by RCQ decoders. HDQ is a low-complexity design technique that is slightly sub-optimal. Simulation results comparing HDQ and an optimal quantizer on the symmetric binary-input memoryless additive white Gaussian noise channel show a loss in mutual information between these two quantizers of less than $10^{-6}$ bits, which is negligible for practical applications.
