Peel Decoding of Concatenated Codes With Selective C3 Correction
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Solution Overview
Problem
Existing decoding techniques for concatenated codes in storage systems are inefficient, as they often introduce errors or require computationally intensive estimation of data portions, which can be detrimental to components like Viterbi decoders and increase processing cycles.
Innovation Solution
The implementation of a peel decoding process that selects and decodes C3 codewords based on their association with uncorrected C1 codewords, utilizing soft information and partial C2 parity to correct errors, thereby avoiding the need for estimating data portions and reducing computational intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If estimation techniques are used to synthesize data portion 102, then error correction capability is improved, but new errors are introduced and processing cycles increase
Solution Approach 1:
The decoding process is segmented into multiple passes: first attempting C1 decoding, then C3 decoding only on failed codewords, and using C2 parity selectively. This segmentation avoids applying estimation techniques globally, thereby preventing widespread introduction of new errors while maintaining error correction capability where needed.
Solution Approach 2:
Instead of applying estimation techniques to all data portions, the patent applies C3 decoding partially only to codewords that failed C1 decoding. This partial action provides sufficient error correction for the problematic cases without subjecting all data to potentially harmful estimation processes, thus reducing new error introduction.
2Reliability
If estimation techniques are used to synthesize data portion 102, then error correction capability is improved, but computational intensity increases
Solution Approach 1:
The computational workload is segmented into three distinct decoding stages (C1, C3, and C2 parity) with C3 decoding applied only to failed codewords. This segmentation reduces overall computational intensity compared to applying estimation techniques universally, while maintaining error correction capability for the necessary cases.
Solution Approach 2:
C3 decoding is applied partially only to codewords that failed C1 decoding rather than to all codewords. This partial application significantly reduces computational intensity and energy consumption compared to global estimation techniques, while providing sufficient error correction capability for the problematic cases.
3Extent of automation
If estimated values are fed into Viterbi decoders, then decoding completeness is improved, but decoder reliability deteriorates
Solution Approach 1:
C3 decoding is performed as a preliminary step before Viterbi decoding, correcting errors in advance. This preliminary action improves decoding completeness by handling errors that C1 missed, while maintaining Viterbi decoder reliability by providing cleaner input data rather than poor estimates.
Solution Approach 2:
C3 decoding acts as an intermediary between C1 decoding and Viterbi decoding. It mediates the error correction process by handling specific error cases that C1 missed, thereby improving overall decoding completeness while protecting Viterbi decoder reliability by providing better-prepared input data.
Data Source
AI summary
A codeword that is associated with one uncorrected codeword in a set of first codewords is selected from a set of third codewords. Error correction decoding is performed on the selected codeword using a third, systematic error correction code.