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International Conference on Information, Communications & Signal Processing (ICICS)

Differentiating trapping sets with the same label [w; u

X. Zheng, Francis C. M. Lau, Chi Kong Tse

Published Dec 1, 2009• 0 citations
Publisher

Abstract

Trapping sets (TSs) have been known to contribute to errors in the decoding of low-density parity-check (LDPC) codes, particularly at the high signal-to-noise ratio (SNR) region. Moreover, TSs with the same label [w; u] are considered equivalent under the automorphism of the graph of a regular code. But according to our our simulations, TSs with the same label [w; u] are producing different error rates in the case of irregular codes. In this paper, we will identify and explain the cause of the differences in error rates for TSs with the same label. Further, we will propose a simple mechanism to differentiate these TSs.

DOI
10.1109/icics.2009.5397616

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Trapping sets (TSs) have been known to contribute to errors in the decoding of low-density parity-check (LDPC) codes, particularly at the high signal-to-noise ratio (SNR) region. Moreover, TSs with the same label [w; u] are considered equivalent under the automorphism of the graph of a regular code. But according to our our simulations, TSs with the same label [w; u] are producing different error rates in the case of irregular codes. In this paper, we will identify and explain the cause of the differences in error rates for TSs with the same label. Further, we will propose a simple mechanism to differentiate these TSs.
Trapping sets (TSs) are known to cause error floors in regular and irregular low-density parity-check (LDPC) codes. By avoiding major error-contributing TSs during the code construction process, codes with low error floors can effectively be built. In it has been shown that TSs labeled as [w;u] are considered as being equivalent under the automorphism of the graph and are therefore contributing equally to the error floor. However, TSs with the same label [w;u] are not identical in general, particularly for the case of irregular LDPC codes. In this paper, we introduce a parameter e that can identify the number of "distinguishable" cycles in the connected subgraph induced by an elementary trapping set. Further, we propose a code construction algorithm, namely the Progressive-Edge-Growth Approximate-minimum-Cycle-Set-Extrinsic-message-degree (PEG-ACSE) method, that aims to avoid small elementary trapping sets (ETSs), particularly detrimental ETSs. We also develop theorems evaluating the minimum possible ETSs formed by PEG construction algorithms in general. We compare the characteristics of the codes built using the proposed method and those built using PEG-only or PEG-Approximate-minimum-Cycle-Extrinsic-message-degree (PEG-ACE) methods. Results from simulations show that the codes constructed using the proposed PEG-ACSE method produce lower error rates, particularly at the high signal-to-noise (SNR) region, compared with codes constructed using other PEG-based algorithms.
Elementary trapping sets (ETSs) have been found to be the main cause of error floor in the decoding of low-density parity-check (LDPC) codes. Moreover, irregular LDPC codes that avoid harmful [w; u; e] ETSs have been constructed and have shown to possess superior error performance compared with other LDPC codes. In this paper, we attempt to evaluate irregular LDPC code performance under an additive white Gaussian noise channel within the high SNR region using the importance sampling (IS) approach in conjunction with the identification of [w; u; e] ETSs. For any given irregular LDPC code, we will first apply a three-step method that aims to search as many ETSs within the code as possible. Then, we will classify these ETSs into different groups based on their labels, i.e., [w; u; e]'s. Further, by dividing the error region into various sub-regions centered by ETSs, we apply the IS simulator to evaluate the error rate of each of the sub-regions. Based on the error rates of all the sub-regions, we can estimate the overall error rate of the LDPC code. Results have indicated that our proposed IS scheme can produce speed-up gains up to 3.9×109 times compared to Monte Carlo simulations.
Trapping sets strongly degrade performance of low-density parity check (LDPC) codes in the low-error-rate region. This creates significant difficulties for the deployment of LDPC codes to low-error-rate applications such as storage and wireless systems with no or limited retransmission options. We propose a novel technique for breaking trapping sets based on collaborative decoding that utilizes two different decoding modes. While the main decoding mode executes message passing based on the original parity check matrix of the corresponding LDPC code, the sub-decoding mode operates on a modified parity check matrix formed by removing a portion of check nodes in the factor graph representation of the given code. The modified parity check matrix is designed to promote a passing of correct information into erroneous variable nodes in the trapping set. Theoretical properties of the proposed trapping-set-breaking technique have been established based on the notion of the improved separation for the trapped variable nodes. Simulation results show that the proposed collaborative LDPC decoding scheme switching between the two decoding modes back and forth effectively breaks dominant trapping sets of various known types of regular and irregular LDPC codes.
The performance of low-density parity-check (LDPC) codes in the error floor region is closely related to some combinatorial structures of the code's Tanner graph, collectively referred to as {\it trapping sets (TSs)}. In this paper, we study the asymptotic average number of different types of trapping sets such as {\em elementary TSs (ETS)}, {\em leafless ETSs (LETS)}, {\em absorbing sets (ABS)}, {\em elementary ABSs (EABS)}, and {\em stopping sets (SS)}, in random variable-regular and irregular LDPC code ensembles. We demonstrate that, regardless of the type of the TS, as the code's length tends to infinity, the average number of a given structure tends to infinity, to a positive constant, or to zero, if the structure contains no cycle, only one cycle, or more than one cycle, respectively. For the case where the structure contains a single cycle, we obtain an estimate of the expected number of the structure through the available approximations for the average number of its constituent cycle. These estimates, which are independent of the block length and only depend on the code's degree distributions, are shown to be accurate even for finite-length codes.
Low density parity-check (LDPC) codes are a class of linear block codes that are decoded by running belief propagation (BP) algorithm or log-likelihood ratio belief propagation (LLR-BP) over the factor graph of the code. One of the disadvantages of LDPC codes is the onset of an error floor at high values of signal to noise ratio caused by trapping sets. In this paper, we propose a two stage decoder to deal with different types of trapping sets. Oscillating trapping sets are taken care by the first stage of the decoder and the elementary trapping sets are handled by the second stage of the decoder. Simulation results on the regular PEG (504,252,3,6) code and the irregular PEG (1024,518,15,8) code shows that the proposed two stage decoder performs significantly better than the standard decoder.