LDPC Check Node Decoding With Linearized Log-Cosh Approximation
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Solution Overview
Problem
Existing LDPC code decoding methods, such as the min-sum algorithm, face performance deterioration, especially for irregular LDPC codes, and require complex calculations and noise variance estimation, which are not efficiently addressed by current algorithms like the sum-product, normalized min-sum, and offset min-sum algorithms.
Innovation Solution
The method involves linearizing or step-approximating the natural logarithm hyperbolic cosine function in the check node updating equation of the sum-product algorithm and converting it to match the check node updating equation of the min-sum algorithm, thereby eliminating the need for noise variance estimation and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the sum-product algorithm is used for decoding LDPC code, then decoding performance is improved, but calculation complexity increases significantly and noise variance estimation is required
Solution Approach 1:
The patent transforms the sum-product algorithm's hyperbolic tangent function into a linear function by changing the mathematical parameter representation. This linearization eliminates complex calculations while preserving the essential decoding functionality, thereby reducing computational complexity without significantly compromising decoding performance
Solution Approach 2:
The patent replaces the complex sum-product algorithm with a simplified min-sum algorithm that uses basic arithmetic operations instead of hyperbolic functions. This substitution uses simpler, computationally cheaper operations that achieve comparable decoding performance without requiring noise variance estimation
2Device complexity
If the min-sum algorithm is used for decoding LDPC code, then calculation complexity is reduced and noise variance estimation is eliminated, but decoding performance deteriorates
Solution Approach 1:
The patent introduces dynamic adjustment mechanisms into the min-sum algorithm, allowing the algorithm to adapt its parameters based on the specific LDPC code structure and channel conditions. This dynamic adaptation enables the simplified algorithm to achieve performance closer to the optimal sum-product algorithm
Solution Approach 2:
The patent modifies the min-sum algorithm by changing key parameters such as the normalization factor and threshold values to optimize performance for irregular LDPC codes. These parameter adjustments allow the algorithm to compensate for its inherent simplifications and reduce performance deterioration
3Reliability
If the normalized min-sum algorithm or offset min-sum algorithm is used, then performance is improved for regular LDPC codes, but performance deteriorates for irregular LDPC codes
Solution Approach 1:
The patent develops a universal decoding algorithm that can effectively handle both regular and irregular LDPC codes through a unified framework. The algorithm incorporates adaptive mechanisms that automatically adjust to different code structures, eliminating the need for separate optimized versions for regular and irregular codes
Solution Approach 2:
The patent employs adaptive parameter selection that changes based on the LDPC code type. The algorithm automatically adjusts normalization factors, threshold values, and other parameters according to whether the code is regular or irregular, thereby maintaining high performance across different code structures
Data Source
AI summary
A low-density parity check (LDPC) code decoding method may be provided. The LDPC code decoding method may linearize or perform step-approximation on a natural logarithm hyperbolic cosine function included in a check node updating equation of a sum-product algorithm used for decoding an LDPC code, and may convert the linearized function to correspond to a check node updating equation of a min-sum algorithm.