Cyclic Code Decoder Using Candidate Syndromes for Simpler Error Correction
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Solution Overview
Problem
Conventional cyclic code decoding methods require complex physical circuits and computations due to the use of soft information and multiple circuits for error location polynomial calculations, making them inefficient for error correction.
Innovation Solution
A cyclic code decoding method utilizing a decoder with a syndrome calculator, error location polynomial generator, and error correction device that identifies unreliable symbols, calculates evaluated syndrome values, and adjusts them to generate an error location polynomial, allowing for error correction with a simpler circuit structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional decoding method based on Chase algorithm is used to decode cyclic codes with soft information, then error correction capability is improved, but computational complexity and device complexity increase significantly
Solution Approach 1:
The patent segments the decoding process into distinct functional modules: syndrome calculator, error location polynomial generator, and error correction device. Each module performs a specific task, allowing the system to handle soft information through structured processing rather than requiring complex unified circuits. The segmentation enables parallel processing paths for different syndrome patterns.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing syndrome patterns and their corresponding error location polynomials. The syndrome calculator computes syndromes in advance, and the error location polynomial generator prepares polynomial expressions before actual error correction is needed. This preliminary processing reduces real-time computational complexity.
2Measurement precision
If multiple physical circuits are used to decode evaluated codewords to obtain candidate codewords, then decoding accuracy is improved, but hardware requirements and device complexity increase
Solution Approach 1:
The patent implements a universal error location polynomial generator that can handle multiple syndrome patterns and generate corresponding error location polynomials using the same hardware circuit. This multi-functional device replaces the need for multiple dedicated circuits for different codeword evaluations, reducing the total number of physical circuits while maintaining decoding accuracy.
Solution Approach 2:
The patent merges the functions of multiple decoding circuits into a single integrated system. The syndrome calculator, error location polynomial generator, and error correction device work together as a unified structure that processes multiple candidate codewords sequentially or in parallel without requiring separate physical circuits for each, thereby reducing hardware requirements.
3Measurement precision
If Berlekamp-Massey algorithm or Euclidean algorithm is used to calculate error location polynomial, then error location accuracy is improved, but physical circuit complexity increases
Solution Approach 1:
The patent changes the parameters of the error location polynomial generation process by using pre-computed syndrome patterns and corresponding polynomial coefficients. Instead of implementing the full Berlekamp-Massey or Euclidean algorithm with its complex iterative calculations, the system uses parameter-based lookup and substitution methods that achieve the same error location accuracy with simpler circuitry.
Data Source
AI summary
In a cyclic code decoding method, a decoder analyzes a received codeword to identify unreliable symbols in the codeword, and sets candidate syndrome patterns accordingly. Then, a syndrome calculator calculates evaluated syndrome values associated with one of the candidate syndrome patterns, and an error location polynomial (ELP) generator generates an ELP according to the syndrome values. An error correction device corrects the errors in the codeword according to the ELP when a degree of the ELP is not more than a threshold value, and the syndrome calculator adjusts the syndrome values and the ELP generator generates another ELP according to the adjusted syndrome values when otherwise.