LDPC Check Node Min-Sum Adjustment for Lower-Cost Decoding
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Solution Overview
Problem
Low-density parity-check (LDPC) codes decoded using iterative min-sum (MS) decoders face performance degradation compared to sum-product algorithm (SPA) decoders, while also incurring higher implementation costs in terms of circuit area, power consumption, and processing cycles.
Innovation Solution
A modified MS decoding approach is implemented, where check nodes adjust the minimum value sent to other nodes by reducing it by an adjustment value when two incoming values are equal, thereby approximating the performance of SPA decoding without the high implementation costs of SPA.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative sum-product algorithm (SPA) decoding is used, then error-correction performance is improved, but implementation cost increases
Solution Approach 1:
The patent modifies the min-sum decoding algorithm by changing the parameter used in message passing from the raw minimum value to an adjusted minimum value (minimum value minus adjustment value). This parameter change allows the simpler MS decoder to achieve performance closer to SPA without requiring the complex logarithmic sum calculations that SPA demands, thus improving error-correction performance while maintaining low implementation cost.
2Device complexity
If iterative min-sum (MS) decoding is used, then implementation cost is reduced, but error-correction performance deteriorates
Solution Approach 1:
The patent introduces an adjustment value that modifies the minimum value parameter exchanged between check nodes and variable nodes. By subtracting this adjustment value from the minimum value, the algorithm compensates for the performance degradation inherent in MS decoding, enabling it to achieve error-correction performance comparable to SPA while retaining the simplicity and low cost of MS implementation.
Solution Approach 2:
The adjustment value acts as a feedback mechanism that compensates for the approximation errors introduced by min-sum decoding. By incorporating this adjustment based on the minimum value and other received values, the system continuously corrects the degradation in performance, allowing MS decoding to approach SPA performance levels.
Data Source
AI summary
Techniques are described for performing a check node update (CNU) as part of iterative decoding of a low density-parity check (LDPC) code. The CNU uses a min-sum decoding approach that monitors whether two values received in messages from two variable nodes connected to a check nodes are equal and are the minimum value among the values received by the check nodes from other variable nodes connected thereto. Upon detecting such an event, the minimum value is adjusted by reducing it by an adjustment value to generate an adjusted minimum value. This adjusted minimum value approximates the minimum value that a sum-product algorithm (SPA) decoding approach would have generated. The adjusted minimum value is included in a response message sent from the check node to a variable node. The bit corresponding to that variable node is decoded based on this adjusted minimum value.