QC-LDPC Vertical Layered Decoding with Predictive Magnitude Maps
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Solution Overview
Problem
Current vertical layered iterative decoders for low-density parity check (LDPC) codes, particularly quasi-cyclic LDPC codes, face high computational and storage complexity, which impacts error correction strength and decoding speed, often requiring significant resource usage and resulting in sub-optimal performance.
Innovation Solution
The implementation of a simplified vertical layered decoder using a predictive magnitude map (PMM) function to generate additional magnitudes, reducing the size of the check node memory and simplifying check-node update processing, while maintaining close approximation to the full magnitude state decoder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full magnitude state is stored in check node memory, then decoding accuracy is improved, but memory size and computational complexity increase significantly
Solution Approach 1:
The patent extracts only the essential magnitude information needed for decoding by storing only the smallest magnitude value in each check node state, rather than storing the complete magnitude state. This extraction approach maintains decoding accuracy while significantly reducing memory requirements and computational complexity.
Solution Approach 2:
The patent uses a predictive magnitude map function that generates approximate magnitude values on-demand rather than storing persistent complete magnitude states. This disposable approximation strategy provides sufficient decoding accuracy with minimal memory storage, trading off complete precision for reduced complexity.
2Reliability
If more magnitudes are stored in check node states, then error correction strength is improved, but resource usage increases
Solution Approach 1:
The patent implements a predictive magnitude map function that autonomously generates the second smallest magnitude value from the stored smallest magnitude and syndrome information, eliminating the need to externally store or transmit additional magnitude data. This self-service mechanism maintains error correction strength while minimizing resource usage.
Solution Approach 2:
The patent changes the representation parameters by storing only the smallest magnitude value and using a predictive function to derive other needed magnitudes, rather than storing multiple magnitude values directly. This parameter transformation reduces the quantity of stored data while preserving error correction capability.
3Productivity
If vertical layered decoding is implemented, then decoding speed is improved, but implementation complexity increases
Solution Approach 1:
The patent segments the decoding process into vertical layers where check nodes are processed in groups, with each layer updating a subset of variable nodes. This segmentation enables parallel processing and improved decoding speed while the simplified magnitude storage reduces the complexity overhead of the layered structure.
Solution Approach 2:
The patent performs preliminary computation of the predictive magnitude map function during the check node update phase, preparing magnitude information in advance for subsequent variable node updates. This preliminary action streamlines the vertical layered decoding process, improving speed without adding significant complexity.
Data Source
AI summary
A method and apparatus for decoding quasi-cyclic LDPC codes using a vertical layered iterative message passing algorithm. The algorithm of the method improves the efficiency of the check node update by using one or more additional magnitudes, predicted with predictive magnitude maps, for the computation of messages and update of the check node states. The method allows reducing the computational complexity, as well as the storage requirements, of the processing units in the check node update. Several embodiments for the apparatus are presented, using one or more predictive magnitude maps, targeting significant savings in resource usage and power consumption, while minimizing the impact on the error correction performance loss.