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March 6, 2026Symmetry0 citationsOpen Access

A Regional Message Scaling Min-Sum Decoding Algorithm for MET-LDPC Codes

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YYYing YouGSGuodong SuWLWeiwei Lin

Key Points

  • The aim is to enhance the performance of MET-LDPC codes using a new decoding algorithm.
  • Proposed a regional message scaling decoding algorithm for MET-LDPC codes.
  • Partitioned edges of the Tanner graph into three functional regions.
  • Introduced cross-region message scaling factors for information flow control.
  • Integrated multi-edge structure and decoding architecture into a unified RMS framework.
  • Achieved a significantly lower error floor compared to traditional min-sum decoding.
  • Demonstrated improved performance across various code lengths in simulations.
  • Showed effectiveness over the additive white Gaussian noise channel.

Abstract

To offer multi-edge type low-density parity-check (MET-LDPC) codes with better performance, this paper proposes a regional message scaling min-sum (RMS) decoding algorithm which improves the performance of the traditional min-sum (MS) decoding algorithm and its modified versions. The contributions of this study are as follows. First, based on the edge-type topology of MET-LDPC codes, we fully exploit their inherent structural information to develop a cross-region decoding architecture by dynamically partitioning the edges of the Tanner graph into three functional regions. Second, we introduce cross-region message scaling (CMS) factors to establish an asymmetric information flow control mechanism, which adaptively regulates the intensity of information exchange across regions. Third, by integrating the multi-edge structure, the cross-region decoding architecture, and the asymmetric information flow control mechanism into a unified framework, we propose the RMS decoding algorithm tailored for MET-LDPC codes. For various code lengths, simulation results demonstrate that the proposed algorithm achieves a significantly lower error floor compared to the traditional MS decoding algorithm and its modified versions over the additive white Gaussian noise (AWGN) channel.

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Cite This Study

You et al. (2026) studied this question.

synapsesocial.com/papers/69aa70c8531e4c4a9ff5ae23https://doi.org/10.3390/sym18030444
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