Polar Code Decoder Kernel Prioritization for Belief Propagation
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Solution Overview
Problem
Polar codes exhibit poor performance for small code sizes, such as N=512, compared to other coding techniques like Turbo codes or LDPC codes, and require optimization for Binary Discrete-input Memory-less Channels, with high computational complexity in decoding due to the large number of recursions in the kernel structure, leading to inefficiencies in beliefs propagation.
Innovation Solution
A method for improving beliefs propagation in polar code decoding by computing a performance-improvement metric for each kernel, sorting them based on this metric, and iteratively updating beliefs to prioritize kernels that provide the most relevant information, thereby optimizing the decoding process while meeting a stop condition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If polar codes use a large number of recursions in the kernel structure to achieve high reliability, then the coding performance improves, but the decoding computational complexity increases significantly
Solution Approach 1:
The patent segments the decoding process by dividing kernels into different types (first-type kernels and second-type kernels) based on their contribution to decoding performance. This segmentation allows the system to prioritize computation on important kernels while reducing or skipping computation on less important kernels, thereby resolving the contradiction between maintaining high reliability through full computation and reducing complexity through selective computation.
Solution Approach 2:
The patent applies local quality by differentiating the processing quality applied to different kernels. First-type kernels receive full beliefs propagation computation to ensure high decoding accuracy, while second-type kernels receive reduced or skipped computation. This local differentiation in computation quality maintains overall decoding performance while reducing total computational complexity.
2Reliability
If polar codes are optimized for specific BDMC characteristics, then the decoding performance improves, but the adaptability to different channel conditions decreases
Solution Approach 1:
The patent introduces dynamics by making the kernel processing strategy adaptive based on channel conditions. The decoder dynamically determines which kernels are first-type and which are second-type based on the specific BDMC characteristics and decoding requirements. This dynamic adaptation allows the system to optimize performance for specific channels while maintaining the capability to adapt to different channel conditions through reconfiguration of the kernel processing strategy.
Data Source
AI summary
A decoder performs: computing (S501) a value (i,j) of a performance-improvement metric for each kernel Ki,j; and sorting (S502) the kernels in a list in decreasing order of the values (i,j). The decoder then performs a beliefs propagation iterative process as follows: updating (S503) output beliefs for the W top kernels of the list , and propagating said output beliefs as input beliefs of the neighbour kernels of said W top kernels; updating (S504) output beliefs for each neighbour kernel of said W top kernels following update of their input beliefs, and re-computing (S505) the performance-improvement metric value (i,j) for each said neighbour kernel; setting (S505) the performance-improvement metric for said W top kernels to a null value; and re-ordering (S506) the kernels in the list . Then, the decoder repeats the beliefs propagation iterative process until a stop condition is met.