QC-LDPC Shift Values With Rate-Specific ACE Constraints
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Solution Overview
Problem
Existing LDPC code designs for New Radio (NR) face challenges in avoiding harmful short cycles with low connectivity in the high-rate part of rate-compatible LDPC codes, despite high ACE values for the full parity-check matrix, and it is difficult to enforce tough ACE constraints for large cycles.
Innovation Solution
A lifting method with different approximate cycle extrinsic message degree (ACE) constraints for different code rates and cycle lengths, optimizing ACE constraints for each shift size separately to ensure short cycles have higher connectivity than longer cycles, and specifying these constraints for systematic and parity bits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single ACE constraint is applied to the full parity-check matrix, then the overall ACE value is improved, but harmful short cycles with low connectivity remain in the high-rate part of the code
Solution Approach 1:
The patent divides the parity-check matrix into multiple submatrices corresponding to different code rates. Each submatrix is optimized with its own ACE constraint tailored to its specific code rate, allowing different parts of the code to have different cycle characteristics. This segmentation enables the high-rate part to have higher ACE constraints that eliminate harmful short cycles, while the overall code maintains good performance across all rates.
Solution Approach 2:
The patent applies different ACE constraints to different submatrices based on their local code rate requirements. Each submatrix receives a locally optimized constraint that addresses the specific harmful cycles present in that code rate portion, rather than applying a uniform global constraint. This local quality approach ensures that each part of the code has the appropriate cycle properties for its intended use.
2Reliability
If tough ACE constraints are applied to large cycles, then long cycle performance is improved, but it becomes difficult to find valid shift coefficients
Solution Approach 1:
The patent makes the ACE constraints dynamic by adjusting them according to the code rate and submatrix size. Rather than applying a static tough constraint to all cycles, the constraint level adapts to the specific submatrix being constructed. This dynamic approach allows tougher constraints to be applied where appropriate (improving long cycle performance) while maintaining feasibility in other areas where finding valid shifts would be too difficult.
Solution Approach 2:
The patent changes the ACE constraint parameter based on the code rate and submatrix characteristics. By adjusting the constraint parameter dynamically rather than keeping it fixed, the system can enforce tougher constraints on long cycles when needed while maintaining ease of coefficient selection in other contexts. This parameter change enables flexible optimization without sacrificing feasibility.
3Reliability
If separate shift coefficient designs are specified for each shift size, then optimal performance for each shift size is achieved, but storage requirements increase
Solution Approach 1:
The patent creates a universal lifting method that can generate optimal shift coefficient designs for multiple shift sizes using a single set of base matrices and systematic procedures. This multi-functional approach allows the same base structures to serve multiple shift sizes, eliminating the need to store separate designs for each shift size while maintaining optimal performance across all supported shift sizes.
Data Source
AI summary
According to some embodiments, a method for use in a wireless transmitter of a wireless communication network comprises encoding information bits using a parity check matrix (PCM) and transmitting the encoded information bits to a wireless receiver. The parity check matrix (PCM) is optimized according to two or more approximate cycle extrinsic message degree (ACE) constraints. In some embodiments, a first portion of the PCM is optimized according to a first ACE constraint and a second portion of the PCM is optimized according to a second ACE constraint.


