Set Partitioning and Multilevel Coding for HIHO Error Reduction
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Solution Overview
Problem
Hard-in, hard-out (HIHO) systems, such as Flash storage systems, face errors due to noise, which existing technologies have not effectively addressed without increasing system complexity or overhead.
Innovation Solution
The implementation of SM-enhancing set partitioning and multilevel coding, where data is encoded and decoded using specific error correcting codes based on signal margin subsets, optimizing error correction capabilities while maintaining low complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing error correction technologies are used in HIHO systems, then error rates can be reduced, but system complexity and overhead information increase
Solution Approach 1:
The invention partitions the set of signal levels into multiple subsets, where each subset is assigned to a different code in a multilevel coding scheme. This segmentation allows each code to operate on a reduced alphabet size, simplifying the error correction process while maintaining overall system reliability. The partitioning enables independent optimization of each subset without increasing overall system complexity.
Solution Approach 2:
The invention introduces a new dimension to error correction by using signal margin information that was previously unused in conventional HIHO systems. By incorporating signal margin subsets into the coding scheme, the system achieves better error correction performance without requiring additional hardware complexity, effectively adding a new degree of freedom to the error correction process.
2Reliability
If existing error correction technologies are used in HIHO systems, then error rates can be reduced, but overhead information increases
Solution Approach 1:
The invention applies different coding strategies to different subsets of signal levels based on their local characteristics, specifically their signal margins. Each subset is encoded with appropriate redundancy levels tailored to its error susceptibility, optimizing the balance between error correction and overhead information for each local region of the signal space rather than applying uniform overhead across all signals.
3Reliability
If SNR gain is achieved through error correction, then reliability improves, but system complexity increases
Solution Approach 1:
The invention enables the system to utilize its own signal margin information, which is already available during normal operation, to improve error correction performance. By incorporating this self-generated information into the coding scheme, the system achieves SNR gain without requiring external assistance or additional complex processing, effectively making the system self-improving.
Data Source
AI summary
A signal is encoded by receiving input data. A first portion of the input data is encoded to obtain a first set of encoded data. At least some part of the input data is processed to determine which one of a plurality of subsets the input data corresponds to. In the event the input data corresponds to a first subset having a greater signal margin (SM) than a second subset, the first set of encoded data and a second portion of the input data are output. In the event the input data corresponds to the second subset, the second portion of the input data is encoded to obtain a second set of encoded data and the first set of encoded data and the second set of encoded data are output.