CLDPC Parity Check Matrix Layout for High-Throughput Decoding
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Solution Overview
Problem
Existing error correction coding techniques for storage devices face challenges in balancing high error correction capability with decoding throughput and data storage density, often resulting in uncorrectable errors due to excessive redundancy or complex encoding/decoding operations.
Innovation Solution
The implementation of convolutional low-density parity-check (CLDPC) codes, which utilize a parity check matrix with multiple copies of a sub-matrix and a terminating portion to simplify operations, enable high decoding throughput and encodeability while maintaining a high error correction capability through windowed decoding processes and multiple decoders with varying power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a relatively large amount of redundancy information is used to increase error correction capability, then error correction capability is improved, but data decoding throughput may decrease due to additional computations
Solution Approach 1:
The parity check matrix is divided into multiple sub-matrices arranged in a structured pattern, allowing the decoding process to be segmented into smaller operations. This segmentation reduces the computational complexity per operation while maintaining the overall error correction capability through the combined effect of multiple sub-matrices.
Solution Approach 2:
The patent uses a moderate amount of redundancy information that provides sufficient error correction capability without excessive computations. The structured LDPC code design achieves near-optimal error correction performance with a balanced redundancy level, avoiding both insufficient correction and excessive computational overhead.
2Reliability
If a relatively large amount of redundancy information is used to increase error correction capability, then error correction capability is improved, but data storage density decreases
Solution Approach 1:
The patent optimizes the redundancy ratio by carefully designing the parity check matrix structure and selecting appropriate code rates. The structured LDPC code achieves high error correction capability with an optimized redundancy level that maintains acceptable storage density, balancing the trade-off between reliability and storage efficiency.
3Reliability
If the number of linear equations associated with a codeword is increased to generate additional redundancy information, then error correction capability is improved, but encoding and decoding operations become more complex
Solution Approach 1:
The parity check matrix is segmented into multiple sub-matrices with a structured arrangement pattern. This segmentation transforms the complex decoding problem into smaller, more manageable operations that can be executed efficiently, reducing the overall computational complexity while maintaining error correction capability.
Solution Approach 2:
The structured LDPC code uses repeated patterns of sub-matrices in the parity check matrix. This copying approach allows the use of standardized decoding algorithms that can be efficiently implemented, reducing complexity compared to designing and implementing unique decoding logic for each individual parity check equation.
4Quantity of substance
If existing ECC techniques are used to increase data storage density by reducing redundancy information, then data storage density is improved, but error correction capability decreases resulting in uncorrectable errors
Solution Approach 1:
The patent optimizes the code rate and redundancy ratio by carefully designing the parity check matrix structure. The structured LDPC code achieves acceptable storage density while maintaining sufficient error correction capability through optimized parameter selection, avoiding the uncorrectable errors that result from excessive redundancy reduction in conventional codes.
Data Source
AI summary
In an illustrative example, a method includes receiving data to be processed in accordance with a convolutional low-density parity-check (CLDPC) code. The method also includes processing the data based on a parity check matrix associated with the CLDPC code. The parity check matrix includes a first portion and a second portion. The first portion includes a plurality of copies of a first sub-matrix that is associated with a first sub-code, and the second portion includes a copy of second sub-matrix that is associated with a second sub-code.