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IEEE Transactions on Communications

Design and Decoding of Irregular LDPC Codes Based on Discrete Message Passing

Michael Meidlinger, Gerald Matz, Andreas Peter Burg

Published Sep 28, 2019• 27 citations
Publisher

Abstract

We consider discrete message passing (MP) decoding of low-density parity check (LDPC) codes based on information-optimal symmetric look-up table (LUT). A link between discrete message labels and the associated log-likelihood ratio values (defined in terms of density evolution distributions) is established. This link gives rise to an algebraic structure on the message labels and leads to an interpretation of LUT decoding as a form of quantized belief propagation. We then exploit the algebraic structure for low-complexity LUT decoder designs. Our LUT decoding framework is the first to also apply to irregular LDPC codes by taking into account the degree distribution in a joint LUT design. We exploit the relation between LUT decoding and belief propagation to obtain stability conditions and irregular LDPC code designs optimized for LUT decoding. The resulting decoders outperform floating-point precision min-sum decoders at LUT resolutions as low as 3 bit s for regular codes and 4 bits for irregular codes.

DOI
10.1109/tcomm.2019.2944159

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Applied Sciences•Oct 11, 2018•17 citations
We consider discrete message passing (MP) decoding of low-density parity check (LDPC) codes based on information-optimal symmetric look-up table (LUT). A link between discrete message labels and the associated log-likelihood ratio values (defined in terms of density evolution distributions) is established. This link gives rise to an algebraic structure on the message labels and leads to an interpretation of LUT decoding as a form of quantized belief propagation. We then exploit the algebraic structure for low-complexity LUT decoder designs. Our LUT decoding framework is the first to also apply to irregular LDPC codes by taking into account the degree distribution in a joint LUT design. We exploit the relation between LUT decoding and belief propagation to obtain stability conditions and irregular LDPC code designs optimized for LUT decoding. The resulting decoders outperform floating-point precision min-sum decoders at LUT resolutions as low as 3 bit s for regular codes and 4 bits for irregular codes.
Irregular low-density parity-check (LDPC) codes are among the best codes currently known. Unfortunately, the performance of existing codes may deteriorate substantially with practical finite-precision decoder implementations. This motivates us to extend our previous work on finite-alphabet decoders based on look-up tables (LUTs) to irregular LDPC codes. We devise a joint design of the LUTs used for different node degrees and present a strategy to optimize the degree distribution (DD) of the LDPC code under LUT decoding. Numerical simulations show that with 4-bit LUT decoding the resulting codes outperform state-of-the-art codes with floating point min-sum (MS) decoding.
We propose a quantized decoding algorithm for low- density parity-check codes where the variable node update rule of the standard min-sum algorithm is replaced with a look-up table (LUT) that is designed using an information-theoretic criterion. We show that even with message resolutions as low as 3 bits, the proposed algorithm can achieve better error rates than a floating-point min-sum decoder. Moreover, we study in detail the effect of different decoder design parameters, like the design SNR and the LUT tree structure on the performance of our decoder, and we propose some complexity reduction techniques, such as LUT re-use and message alphabet downsizing.
In practical implementations, message passing decoding of LDPC codes has to be implemented with finite precision, i.e., the messages are quantized with a small number of bits. This results in a significant performance degradation with respect to decoding with high-precision messages. Recently, we have proposed so-called information bottleneck decoders to design finite-precision decoders with error-correction performance close to high-precision belief-propagation decoding. Earlier works solely focus on the design of information bottleneck decoders for regular LDPC codes or specifically optimized irregular LDPC codes. In this paper, we extend the concept of information bottleneck decoders to irregular LDPC with arbitrary degree distribution. We show that this extension is not straightforward and requires an additional information-optimum step. Therefore, we devise a novel intermediate construction step which we call message alignment. Exemplary numerical simulations using an irregular LDPC code taken from the IEEE 802.11 standard show that incorporating message alignment in the construction yields a 4-bit information bottleneck decoder which performs only 0.15 dB worse than a double-precision belief propagation decoder and outperforms a min-sum decoder.
For decoding low-density parity-check (LDPC) codes on discrete memoryless channels, a method to quantize messages and to find message-passing decoding functions for the variable and check nodes is developed. These are used to obtain noise thresholds by density evolution. The message-passing decoding alphabet is restricted to be discrete with a fixed maximum alphabet size. Discrete quantization is required to obtain this fixed alphabet size; a greedy algorithm which uses the mutual information between the code bit and message is presented. It is argued that using this message-passing decoding framework is more efficient for approaching channel capacity than simply quantizing the belief-propagation algorithm. This method is evaluated using regular LDPC codes on the binary symmetric channel. Using a maximum alphabet size of 16 (4 bits), noise thresholds close to those of belief propagation are obtained.
The Belief-propagation (BP) algorithm is a powerful decoder for LDPC codes. However, the BP algorithm requires the high complexity of the check node operation. Therefore, in the previous works, a lookup table (LUT)-based decoder has been proposed for LDPC codes. In this work, we propose the LUT design for protograph LDPC codes. Our LUT design can support the parallel edge, variable node with degree-1 and punctured variable node. As a result, the coding losses of 2-, 3- and 4-bit quantization of LUT decoder are 1.6, 0.5 and 0.1 dB compared to BP algorithm.
In high-throughput applications, low-complexity and low-latency channel decoders are inevitable. Hence, for low-density parity-check (LDPC) codes, message passing decoding has to be implemented with coarse quantization—that is, the exchanged beliefs are quantized with a small number of bits. This can result in a significant performance degradation with respect to decoding with high-precision messages. Recently, so-called information-bottleneck decoders were proposed which leverage a machine learning framework (i.e., the information bottleneck method) to design coarse-precision decoders with error-correction performance close to high-precision belief-propagation decoding. In these decoders, all conventional arithmetic operations are replaced by look-up operations. Irregular LDPC codes for next-generation fiber optical communication systems are characterized by high code rates and large maximum node degrees. Consequently, the implementation complexity is mainly influenced by the memory required to store the look-up tables. In this paper, we show that the complexity of information-bottleneck decoders remains manageable for irregular LDPC codes if our proposed construction approach is deployed. Furthermore, we reveal that in order to design information bottleneck decoders for arbitrary degree distributions, an intermediate construction step which we call message alignment has to be included. Exemplary numerical simulations show that incorporating message alignment in the construction yields a 4-bit information bottleneck decoder which performs only 0.15 dB worse than a double-precision belief propagation decoder and outperforms a min-sum decoder.