Sparse Graph Code Decoding with Hybrid Erasure Recovery
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Solution Overview
Problem
Current maximum likelihood decoding methods for sparse graph codes, such as Gauss elimination, incur high computational complexity, limiting their applicability and power efficiency in error correction for erasure channels.
Innovation Solution
Implementing a hybrid decoding method that combines trivial decoding and Gauss elimination, where trivial decoding is used to restore lost data with minimal operations and Gauss elimination is applied selectively to reduce overall computational complexity, thereby enhancing error correction capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Gauss elimination method is used for maximum likelihood decoding, then decoding accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the decoding process into two distinct phases: trivial decoding that handles easily recoverable lost packets using simple substitution, and Gauss elimination decoding that handles remaining complex cases. This segmentation allows the system to achieve maximum likelihood decoding accuracy while minimizing computational complexity by applying the computationally intensive Gauss elimination method only when necessary, rather than to all lost packets.
2Reliability
If code length is increased to improve error correction performance, then tolerance to error is improved, but decoding complexity increases
Solution Approach 1:
The patent applies partial action by using trivial decoding for the majority of lost packet recovery cases, which requires minimal computational resources. Only when trivial decoding cannot resolve the lost packets does the system activate Gauss elimination decoding. This partial application of the more complex algorithm maintains error correction capability for long codes while avoiding the full computational burden that would otherwise be required.
Data Source
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AI summary
It is an object of the present invention to make it possible to implement maximum likelihood decoding of a sparse graph code at low computational complexity in the maximum likelihood decoding of the sparse graph code. According to the present invention, in the maximum likelihood decoding of the sparse graph code, a lost data decoding process by a trivial decoding method and a lost data decoding process by a Gauss elimination method are performed repeatedly and alternately.