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IEICE Technical Report; IEICE Tech. Rep.

An improvement of sum-product decoding of LDPC codes by a local search method

Yuki Kawauchi, Hiroshi Enoki, Kohtaro Watanabe, Seiji Kataoka

Published Mar 3, 2016• 0 citations
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An improvement of decoding time of ordered statistic decoding for medium length LDPC codes

Yuki Kawauchi, Kohtaro Watanabe, Seiji Kataoka

It is well-known that the Sum-Product Algorithm (SPA) is an efficient algorithm for decoding Low Density Parity Check (LDPC) codes. In spite of this, for LDPC codes with medium length which are now attracting interests for the application to fifth generation systems, it is known that decoding error rate is much inferior to that of Maximum Likelihood Decoding (MLD). In this paper, we improve bit error rate of SPA with so called Ordered Statistic Decoding (OSD) method by Fossorier [1]D Especially, we improve stopping condition of combinatorial search in OSD method and demonstrate by simulation that the proposal stopping condition is more efficient compared to Fossorier (IEEE J. Sel. Areas Commun., 19, 2001). For that purpose, we introduce a conception “coincidence rate” which plays an important role.

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A Modified Sum-Product Algorithm for Error Floor Reduction in LDPC Codes

Un-Hee Park, Dae-Sub Oh, Youngmin Kim +1 more

In this paper, a modified sum-product algorithm to correct bit errors captured within the trapping sets, which are produced in decoding of low-density parity-check (LDPC) codes, is proposed. Unlike the original sum-product algorithm, the proposed decoding method consists of two stages. Whether the main cause of decoding failure is the trapping sets or not is determined at the first stage. And the bit errors within the trapping sets are corrected at the second stage. In the modified algorithm, the set of failed check nodes and the transition patterns of hard-decision bits are exploited to search variable nodes in the trapping sets. After inverting information of the variable nodes, the sum-product algorithm is carried out to correct the bit errors. As a result of simulation, the proposed algorithm shows continuously improved error performance with increase in the signal-to-noise ratio. It is, therefore, considered that the modified sum-product algorithm significantly reduces or possibly eliminates the error floor in LDPC codes.

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Efficient Inplementation of the Sum-Product Algorithm for Decoding LDPC Codes

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Detecting Minimum Distance of LDPC Codes with Improved Approximately Nearest Codeword Method

Zhewei Tong, Kewu Peng, Zhitong He

Approximately nearest codeword (ANC) is a classic approach for detecting minimum distance of low-density parity-check (LDPC) codes. With the principle of local search, ANC method is capable of detecting minimum distance of LDPC codes within acceptable time. However, the message passing (MP) decoding algorithm and scheduling scheme, as well as the local search strategy, still have much space to improve. To fill the gap, this paper proposes an improved-ANC method which employs novel noise introduction method, decoding algorithm, scheduling scheme and searching strategy, to accelerate the computation of the minimum distance of LDPC codes significantly. Finally, we run simulations on LDPC codes in Wi-Fi5 and 5G-NR standards. The simulation results are presented and demonstrate the effectiveness of our proposal.

International Conference on Electrical Engineering and Photonics (EExPolytech)•Oct 20, 2022•1 citation
It is well-known that the Sum-Product Algorithm (SPA) is an efficient algorithm for decoding Low Density Parity Check (LDPC) codes. In spite of this, for LDPC codes with medium length which are now attracting interests for the application to fifth generation systems, it is known that decoding error rate is much inferior to that of Maximum Likelihood Decoding (MLD). In this paper, we improve bit error rate of SPA with so called Ordered Statistic Decoding (OSD) method by Fossorier [1]D Especially, we improve stopping condition of combinatorial search in OSD method and demonstrate by simulation that the proposal stopping condition is more efficient compared to Fossorier (IEEE J. Sel. Areas Commun., 19, 2001). For that purpose, we introduce a conception “coincidence rate” which plays an important role.
In this paper, a modified sum-product algorithm to correct bit errors captured within the trapping sets, which are produced in decoding of low-density parity-check (LDPC) codes, is proposed. Unlike the original sum-product algorithm, the proposed decoding method consists of two stages. Whether the main cause of decoding failure is the trapping sets or not is determined at the first stage. And the bit errors within the trapping sets are corrected at the second stage. In the modified algorithm, the set of failed check nodes and the transition patterns of hard-decision bits are exploited to search variable nodes in the trapping sets. After inverting information of the variable nodes, the sum-product algorithm is carried out to correct the bit errors. As a result of simulation, the proposed algorithm shows continuously improved error performance with increase in the signal-to-noise ratio. It is, therefore, considered that the modified sum-product algorithm significantly reduces or possibly eliminates the error floor in LDPC codes.
Approximately nearest codeword (ANC) is a classic approach for detecting minimum distance of low-density parity-check (LDPC) codes. With the principle of local search, ANC method is capable of detecting minimum distance of LDPC codes within acceptable time. However, the message passing (MP) decoding algorithm and scheduling scheme, as well as the local search strategy, still have much space to improve. To fill the gap, this paper proposes an improved-ANC method which employs novel noise introduction method, decoding algorithm, scheduling scheme and searching strategy, to accelerate the computation of the minimum distance of LDPC codes significantly. Finally, we run simulations on LDPC codes in Wi-Fi5 and 5G-NR standards. The simulation results are presented and demonstrate the effectiveness of our proposal.