Polar Code List Decoding With Bit Reliability Classification
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Solution Overview
Problem
Existing polar code decoding algorithms, such as SC and SCL, face high calculation and sorting complexities due to sequential decision-making and extensive path analysis, leading to suboptimal decoding performance and error propagation.
Innovation Solution
A decoding method that classifies bits into reliable and unreliable categories based on prior and posterior LLRs, allowing for parallel decoding with path priority, which reduces calculation complexity by considering both posterior and prior information for path metric calculation and sorting only split paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SCL decoding algorithm is used to improve decoding performance, then decoding performance is improved, but calculation complexity and sorting complexity are high
Solution Approach 1:
The patent segments the decoding paths into multiple groups based on path metric values, and only performs sorting on the top L paths rather than all paths. This segmentation approach maintains decoding performance by focusing computational resources on the most promising paths while reducing overall sorting complexity.
Solution Approach 2:
The patent applies partial sorting by only sorting a subset of paths (the top L paths with smallest path metrics) rather than sorting all possible paths. This partial action approach achieves sufficient decoding performance without the full computational burden of complete path sorting.
2Measurement precision
If path sorting is performed for all paths in SCL algorithm, then optimal path selection is improved, but sorting complexity increases
Solution Approach 1:
The patent divides all decoding paths into multiple groups based on their path metric values, identifying the top L paths with the smallest metrics. This segmentation allows precise assessment of path quality for the most promising candidates while avoiding the computational overhead of sorting all paths.
Solution Approach 2:
The patent performs sorting operation only on a partial set of paths (the top L paths) rather than all paths. This partial sorting maintains sufficient measurement precision for path quality assessment while significantly reducing sorting complexity.
3Device complexity
If sequential decision-making is used in SC algorithm, then calculation complexity is reduced, but error propagation occurs and decoding performance deteriorates
Solution Approach 1:
The patent performs preliminary classification of information bits into reliable and unreliable subsets before decoding. This preliminary action allows the system to apply different decoding strategies (SC for reliable bits, SCL for unreliable bits) to different bit subsets, preventing error propagation from unreliable bits while maintaining low complexity for reliable bits.
Solution Approach 2:
The patent applies different decoding quality levels to different parts of the code based on bit reliability. Reliable bits use simple SC decoding, while unreliable bits use more complex SCL decoding with path sorting. This local quality approach optimizes overall decoding performance while managing computational complexity.
Data Source
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AI summary
A decoding method and device, and a decoder are provided. The method includes: receiving N LLRs corresponding to a to-be-decoded signal, where N is a code length (S101); classifying K decoded bits into reliable bits and unreliable bits based on a prior LLR and/or a posterior LLR (S102); generating M decoding paths based on the N LLRs and a preset rule (S103); and selecting each stage of target decoding path based on PM values of the M decoding paths, to obtain a decoding result of each stage of decoded bit (S104).