Enhanced Bit-Flipping Decoding Using Refined Checksum Weights and Flip Metrics for LDPC Codes

Bit-flipping (BF) algorithms for decoding low-density parity-check (LDPC) codes have attracted considerable attention due to their low complexity and high parallelism. While recent improvements have enabled certain BF decoders to approach the performance of belief-propagation (BP) decoding, many of these enhancements rely on complex mechanisms such as loop detection, decoder switching, or costly online computations. In this paper, we propose enhanced BF decoding algorithms that integrate refined checksum weighting and flipping function designs. The proposed weighting scheme jointly exploits soft-valued channel information and hard-decision consistency to better reflect the reliability of each checksum, while maintaining negligible online complexity. Additionally, we propose a flipping function design that implicitly discourages repeated flipping by incorporating the accumulated flip count into its computation, thus suppressing decoding loops without requiring explicit loop detection or decoder switching. Simulation results demonstrate that the proposed decoding strategies yield notable performance gains over existing BF decoders, with only a minor complexity increase.

Authors

Institutions

Publication Details

Published
2025-08-20
DOI
https://doi.org/10.1109/apwcs67981.2025.11151954
Primary Topic
Error Correcting Code Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhanced Bit-Flipping Decoding Using Refined Checksum Weights and Flip Metrics for LDPC Codes

Pei-Yuan Chou, Tofar C.-Y. Chang, Chun-Tao Lin, Kao-Chêng Lin
Error Correcting Code Techniques
article

Enhanced Bit-Flipping Decoding Using Refined Checksum Weights and Flip Metrics for LDPC Codes

Pei-Yuan Chou, Tofar C.-Y. Chang, Chun-Tao Lin, Kao-Chêng Lin
article en

Abstract

Bit-flipping (BF) algorithms for decoding low-density parity-check (LDPC) codes have attracted considerable attention due to their low complexity and high parallelism. While recent improvements have enabled certain BF decoders to approach the performance of belief-propagation (BP) decoding, many of these enhancements rely on complex mechanisms such as loop detection, decoder switching, or costly online computations. In this paper, we propose enhanced BF decoding algorithms that integrate refined checksum weighting and flipping function designs. The proposed weighting scheme jointly exploits soft-valued channel information and hard-decision consistency to better reflect the reliability of each checksum, while maintaining negligible online complexity. Additionally, we propose a flipping function design that implicitly discourages repeated flipping by incorporating the accumulated flip count into its computation, thus suppressing decoding loops without requiring explicit loop detection or decoder switching. Simulation results demonstrate that the proposed decoding strategies yield notable performance gains over existing BF decoders, with only a minor complexity increase.

National Taipei University of Technology (TW), National Taipei University (TW)
Openalex Percentile: Top 72%
Error Correcting Code Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.