Hybrid LDPC Decoding for Lower Check Node Update Complexity
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Solution Overview
Problem
Current LDPC decoding schemes face high complexity, leading to reduced bit and frame error rate performance, particularly due to the computational burden of the check node update process in iterative decoding algorithms.
Innovation Solution
A hybrid decoding scheme is proposed, combining the Belief Propagation algorithm, Min-Sum algorithm, and Lambda-Min algorithm to selectively use bit node messages and algorithms for check node updates, optimizing computational resources and maintaining performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Belief Propagation algorithm is used for check node updates in LDPC decoding, then decoding accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the set of bit node messages into multiple subsets based on their reliability metrics. Different subsets are processed using different algorithms (Belief Propagation for high-reliability messages, Min-Sum for low-reliability messages), thereby applying computational resources selectively rather than uniformly to all messages.
Solution Approach 2:
The patent applies different decoding algorithms to different subsets of messages based on their local characteristics (reliability metrics). High-reliability messages receive the computationally intensive Belief Propagation treatment, while low-reliability messages use the simpler Min-Sum algorithm, optimizing the overall system performance-complexity tradeoff.
2Reliability
If all bit node messages are processed using the Belief Propagation algorithm, then bit error rate performance is improved, but frame error rate performance degrades due to excessive complexity
Solution Approach 1:
The patent applies the computationally intensive Belief Propagation algorithm only partially - specifically to the most reliable subset of bit node messages rather than all messages. This partial application achieves the necessary error correction for the most critical messages while avoiding the excessive complexity that would result from processing all messages with the same algorithm.
Solution Approach 2:
The patent changes the algorithm selection parameter based on the reliability metric of each message subset. By dynamically selecting which algorithm to apply based on message reliability parameters, the system optimizes the balance between error rate performance and computational complexity.
3Device complexity
If a simple decoding algorithm is used to reduce complexity, then device complexity is reduced, but decoding performance deteriorates
Solution Approach 1:
The patent applies different decoding algorithms to different subsets of bit node messages based on their local characteristics (reliability metrics). The most reliable messages receive the computationally intensive Belief Propagation treatment, while less reliable messages use the simpler Min-Sum algorithm, optimizing the overall system performance-complexity tradeoff.
Solution Approach 2:
The patent introduces dynamic algorithm selection based on message reliability metrics. The decoding approach is not static but adapts to the characteristics of each message subset, selecting the appropriate algorithm (Belief Propagation or Min-Sum) based on the reliability of the messages being processed.
Data Source
AI summary
An apparatus and a method for receiving a signal in a communication system using a Low Density Parity Check (LDPC) code. The apparatus and the method includes decoding a received signal according to a hybrid decoding scheme, wherein the hybrid decoding scheme is generated by combining two of a first decoding scheme, a second decoding scheme, and a third decoding scheme.