LDPC Check-Node Scheduling Using Cost Functions for Faster Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing LDPC decoding techniques face inefficiencies in processing time and number of iterations, particularly in handling distorted LDPC encoded data, as they often rely on flooding or sequential scheduling methods that do not effectively prioritize processing based on error correction needs.
Innovation Solution
Implementing a cost function-based approach to selectively schedule the processing of check nodes, where a cost function evaluates and prioritizes groups of check nodes based on reliability values and unsatisfied parity checks, allowing for more focused error correction decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If flooding scheduling or sequential scheduling methods are used for LDPC decoding, then the decoding process can be completed, but the processing time and number of iterations are excessive
Solution Approach 1:
The patent changes the scheduling parameter from fixed (flooding) or simple sequential order to a dynamic cost-function-based selection mechanism. The cost function evaluates multiple parameters including reliability values, unsatisfied check nodes, and iteration history to determine the optimal processing order of check nodes, thereby reducing iterations and processing time while improving decoding efficiency
Solution Approach 2:
The patent introduces dynamic scheduling where the processing order of check nodes changes based on real-time decoding state. The cost function is recalculated after each iteration based on current reliability values and unsatisfied check node status, making the scheduling adaptive and dynamic rather than static, which reduces the number of iterations needed for convergence
2Reliability
If all check nodes are processed in parallel or sequential order, then decoding can proceed, but error correction effectiveness is reduced due to lack of prioritization
Solution Approach 1:
The patent applies local quality by treating different check nodes differently based on their specific characteristics. The cost function evaluates each check node's local state (reliability value, number of unsatisfied check nodes, connection patterns) and assigns different processing priorities accordingly, rather than treating all check nodes uniformly, which improves error correction effectiveness
Solution Approach 2:
The patent implements feedback mechanisms where the cost function continuously monitors decoding progress through reliability values and unsatisfied check node status. This feedback loop allows the scheduler to adapt the processing order based on actual decoding performance, improving error correction effectiveness while the systematic cost function evaluation manages the complexity
Data Source
AI summary
Data is decoded by obtaining a cost function. For each of a plurality of groups of check nodes associated with low-density parity-check (LDPC) encoded data, the cost function is evaluated using information associated with a variable node and/or information associated with a check node. One of the groups of check nodes is selecting based at least in part on the evaluated cost functions. Processing related to error correction decoding is performed on the selected group of check nodes.