LDPC Min-Sum Decoder Correction for Low-Power Iterative Decoding
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Solution Overview
Problem
Iterative error correcting decoders, such as LDPC decoders, face a trade-off between the number of iterations and power complexity, with higher iterations improving bit error rate but increasing power consumption, and existing min-sum algorithms suffer from lower decoding gain and higher logic complexity.
Innovation Solution
Implementing a min-sum with correction algorithm that reduces message resolution and logic complexity by using a correction factor, such as p·2q, to bring performance closer to sum-product algorithms without compromising performance, specifically by adjusting the parity check node outputs based on input magnitudes and thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of iterations is increased to improve bit error rate, then decoding performance is improved, but power consumption increases
Solution Approach 1:
The patent changes the parameter of message resolution from high precision to reduced precision (e.g., from multiple bits to single bit or fewer bits) while maintaining decoding performance. This parameter change allows the decoder to achieve the same bit error rate with fewer computational operations, thereby reducing power consumption without sacrificing reliability
2Use of energy by moving object
If min-sum algorithm is used to reduce power complexity, then power consumption is reduced, but decoding gain decreases
Solution Approach 1:
The patent modifies the min-sum algorithm by changing the parameter of message representation to reduced resolution (lower precision). This parameter change enables the algorithm to maintain decoding gain comparable to sum-product algorithms while keeping power complexity low, as the reduced precision simplifies the computational operations without fundamentally compromising the decoding accuracy
3Device complexity
If message resolution is reduced to lower logic complexity, then device complexity is reduced, but decoding performance may be compromised
Solution Approach 1:
The patent changes the parameter of message resolution to a reduced value (lower precision) and demonstrates that this parameter change does not compromise decoding performance when used with the modified min-sum algorithm. The reduced resolution simplifies the logic complexity by requiring fewer bits for message representation and simpler computational operations, while the algorithm modification ensures that decoding performance is maintained
Data Source
AI summary
An iterative decoder configured to implement a min-sum with correction algorithm. The iterative decoder includes N parity check nodes coupled to M equality constraint nodes. The iterative decoder further includes a first parity check node configured to send an output to a first equality constraint node. Responsive to a minimum magnitude of other M−1 inputs to the first parity check node being lower than a pre-determined threshold, the parity check node sends the output having a same magnitude as that of the minimum magnitude of the other M−1 inputs to the first parity check node. Responsive to the minimum magnitude of the other M−1 inputs to the first parity check node being greater than the pre-determined threshold, the parity check node subtracts a correction factor in the form of p·2q from the minimum magnitude.