LDPC Decoder Parity Reuse for Trellis Path Pruning
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Solution Overview
Problem
Existing data processing systems require multiple power-consuming detection and decoding processes to recover data, often resulting in inefficiencies and failure to achieve desired outputs due to the need for two or more data detection/decoding processes.
Innovation Solution
The implementation of a data processing system that includes a data detection circuit for pruning trellis data paths based on parity equations, an ungrouping circuit to re-create LDPC codewords, and a decoding circuit performing LDPC decoding algorithms, which re-uses decoder parity to enhance data detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple data detection/decoding processes are used to recover data, then data recovery reliability is improved, but power consumption increases and operational effectiveness decreases
Solution Approach 1:
The patent combines the data detection process and decoding process into a unified iterative framework where the detector and decoder share information and cooperate to recover data. This merging allows the system to achieve reliable data recovery through a single integrated process rather than multiple separate processes, thereby reducing power consumption while maintaining reliability.
Solution Approach 2:
The patent implements feedback mechanisms where the decoder provides information back to the detector and vice versa in an iterative manner. This feedback allows each process to leverage the results of the other, improving overall reliability while avoiding the need for completely separate detection and decoding passes, thus reducing total power consumption.
2Reliability
If multiple data detection/decoding processes are performed, then data recovery accuracy is improved, but processing time increases
Solution Approach 1:
By merging detection and decoding into a single iterative process that operates simultaneously rather than sequentially, the patent reduces the total processing time required for data recovery while maintaining high accuracy through the collaborative refinement of estimates across iterations.
Solution Approach 2:
The iterative detection-decoding framework maintains continuous useful action by repeatedly refining data estimates without idle periods between separate detection and decoding passes. Each iteration builds upon the previous one, continuously improving accuracy while minimizing processing time through uninterrupted collaborative refinement.
3Productivity
If trellis pruning is performed using parity equations, then detection efficiency is improved, but the complexity of the detection circuit increases
Solution Approach 1:
The patent makes the parity check circuit perform dual functions: it generates parity checks for decoding and simultaneously provides pruning information for the detector. This multi-functionality allows trellis pruning to be implemented without adding separate dedicated pruning hardware, thereby improving detection efficiency while limiting the increase in circuit complexity.
Solution Approach 2:
The detection circuit uses its own internally generated parity information to perform trellis pruning, rather than requiring external or separate pruning mechanisms. This self-service approach allows the circuit to improve its own detection efficiency using resources already present in the system, minimizing the increase in overall complexity.
Data Source
AI summary
Various approaches related to systems and methods for reusing decoding parity.