Monte carlo metric sc-flip decoding method based on llr absolute value interval

By using a Monte Carlo metric SC-Flip decoding method based on the absolute value range of LLR, the accuracy and computational complexity issues of the SC-Flip decoding algorithm in short to medium code lengths are solved, achieving efficient improvement in error correction performance and reduction in computational complexity.

CN115865104BActive Publication Date: 2026-06-26CHINA UNIV OF GEOSCIENCES (WUHAN)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA UNIV OF GEOSCIENCES (WUHAN)
Filing Date
2022-11-24
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing SC-Flip decoding algorithms suffer from insufficient accuracy and high computational workload when identifying channels, especially with short to medium code lengths where performance is severely compromised. Therefore, it is necessary to improve the accuracy of channel identification and reduce the computational workload.

Method used

A Monte Carlo metric SC-Flip decoding method based on the absolute value interval of LLR is adopted. The information bits and frozen bits are divided by the GA construction algorithm. The error probability of each partition interval is obtained by Monte Carlo simulation, the Monte Carlo metric is simplified, and SC decoding and CRC check are performed according to the error probability to optimize the selection of the flip index.

Benefits of technology

While reducing computational workload, it significantly improves error correction performance, especially with low flip counts, showing a marked performance improvement compared to traditional methods. Moreover, it only requires addition operations, making it easy to implement in hardware.

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Abstract

The application discloses a Monte Carlo metric SC-Flip decoding method based on LLR absolute value interval, and the method comprises the following steps: according to a GA construction algorithm, a bit channel is divided into information bits and frozen bits; LLR absolute value intervals are divided into subintervals, and error probabilities of the subintervals are obtained through Monte Carlo simulation; Monte Carlo metrics are simplified, and the simplified Monte Carlo metric values are calculated according to the error probabilities of the subintervals; SC decoding is performed according to root node LLR values received from a receiver, if CRC check is successful, decoding is ended, otherwise leaf node LLR values are arranged in descending order of the Monte Carlo metric values, flip indexes of the first T Monte Carlo metric values are obtained; unflipped index positions are found from the T flip index positions in sequence to redecode, if CRC check is successful or T times of decoding are completed, decoding is ended. The simplified Monte Carlo metric only needs to perform addition operation, and is convenient for hardware implementation; and error correction performance is not lost; and at low flip times, error correction performance is obviously improved.
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