Polar Code SC-Flip Decoding With Critical-Set Memory Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The successive cancellation (SC) algorithm for polar codes has limitations in error-correcting performance compared to LDPC or turbo codes, necessitating improved decoding methods like SC-Flip, which requires more memory for storing and accessing primary decoding results due to recursive re-decoding.
Innovation Solution
A method and apparatus for decoding polar codes that generate a decoding tree, divide it into partitions, determine memory size based on critical sets, and use SC decoding with storage of specific information for re-decoding, flipping bits with the lowest log likelihood ratio when CRC detection fails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SC-Flip decoding with recursive re-decoding is used, then error correction performance is improved, but memory usage increases due to storing primary decoding results
Solution Approach 1:
The patent extracts and stores only the critical information needed for re-decoding (LLR values and bit positions) rather than storing complete primary decoding results. This selective extraction reduces memory usage while maintaining the ability to perform recursive re-decoding for error correction.
Solution Approach 2:
The patent applies different storage strategies to different parts of the decoding process. Critical sets of bits that require re-decoding are stored with their LLR values and positions, while other bits are processed without full storage. This localized quality approach optimizes memory usage by storing only what is necessary for error correction.
2Measurement precision
If complete primary decoding results are stored for re-decoding, then decoding accuracy is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential elements (LLR values and bit positions) from complete decoding results, creating a simplified data structure that maintains decoding accuracy while reducing memory requirements and structural complexity.
Solution Approach 2:
Instead of storing complete decoding results and then accessing specific parts, the patent inverts the approach by directly storing only the critical LLR values and positions needed for re-decoding, eliminating the need to process and navigate through complete result structures.
Data Source
AI summary
A polar code decoding apparatus according to an embodiment includes a divider configured to generate a decoding tree in which a plurality of nodes including one or more critical sets for a polar-encoded codeword are formed in a hierarchical structure, and divide the decoding tree into one or more partitions, each partition equally including lowest nodes of the decoding tree, a determiner configured to determine a memory size for storing a primary decoding result based on a specific partition, the specific partition being selected from among the one or more partitions based on the number of critical sets included in each partition, and a decoder configured to decode the codeword primarily by using a successive cancellation (SC) decoding technique.