LDPC Decoder Stopping Criterion for Faster Reliable Convergence
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Solution Overview
Problem
Existing LDPC decoding technologies face challenges in achieving an ideal compromise between decoding performance, data rate, implementation complexity, and energy consumption, particularly in high-bit-rate communication systems like space communications, due to suboptimal stopping criteria and data quantization strategies.
Innovation Solution
A novel method for LDPC decoding that includes a stopping criterion based on evaluating the number of iterations where all partial syndromes are zero minus the number of non-zero syndromes exceeding a threshold, combined with an 'Adapted Offset Min-Sum' algorithm for parity check message calculation and on-the-fly data scaling to manage saturation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conventional stopping criterion checking all partial syndromes are zero is used, then decoding reliability is improved, but decoding latency increases and throughput decreases
Solution Approach 1:
The patent extracts only the essential information needed for reliable stopping decision by counting the number of non-zero partial syndromes rather than checking all syndromes individually. This reduces the verification complexity and allows earlier termination while maintaining reliability through the threshold-based stopping criterion.
Solution Approach 2:
The patent changes the stopping criterion from a binary check (all zero or not) to a quantitative parameter-based decision (counting non-zero syndromes and comparing to threshold). This parameter transformation enables more flexible and efficient stopping decisions that balance reliability and latency.
2Device complexity
If Min-Sum algorithm is used to reduce computational complexity, then device complexity is reduced, but decoding performance deteriorates due to overestimation of parity check messages
Solution Approach 1:
The patent introduces a scaling parameter α to adjust the magnitude of parity check messages in the Min-Sum algorithm. By dynamically scaling the messages based on the layer index and code properties, the algorithm compensates for the overestimation effect while maintaining the computational simplicity of Min-Sum, thus improving decoding performance without significantly increasing complexity.
3Device complexity
If data quantization is applied to reduce memory usage and simplify implementation, then device complexity is reduced, but error rate increases due to saturation of data values
Solution Approach 1:
The patent applies dynamic scaling to the quantized data values during the decoding process. The scaling factor adjusts the range of quantized values to prevent saturation while maintaining fixed-point representation benefits. This dynamic adaptation allows the system to maintain low complexity with fixed-point arithmetic while significantly reducing the error floor caused by quantization saturation.
Data Source
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AI summary
The invention relates to an LDPC decoding method (100). The LDPC code is defined by a parity matrix having a layered structure. The method (100) involves performing iterations until a stop criterion is satisfied. Each iteration involves the successive processing of the different layers. Processing a layer involves: - computing (111) variable messages (αn,m), - computing (112) parity check messages (βm,n), - computing (113) a posteriori estimation variables (γn), - computing (114) a partial syndrome. The evaluation (130) of the stop criterion involves checking if, for a plurality of successive iterations, the number of iterations for which all the partial syndromes are zero, from which the number of iterations for which at least one of the partial syndromes is non-zero is subtracted, is greater than or equal to a predetermined stop threshold.