Polar Code Decoding Path Pruning for Lower Sorting Complexity
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Solution Overview
Problem
The existing successive cancellation (SC) decoding algorithm for polar codes has high calculation and sorting complexity due to the spread of errors and the need to calculate and sort numerous decoding paths, which affects decoding performance.
Innovation Solution
A decoding method that classifies bits into reliable and unreliable categories based on prior and posterior log-likelihood ratios (LLRs), generating a limited number of decoding paths and prioritizing parallel decoding to reduce complexity while ensuring good performance, using path metrics that consider both posterior and prior information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the SCL decoding algorithm is used to improve decoding performance, then decoding performance is improved, but calculation complexity and sorting complexity increase
Solution Approach 1:
The patent segments the decoding paths into multiple parallel paths, where each path processes a subset of bits. By dividing the full decoding process into multiple independent parallel paths, the algorithm reduces the sorting complexity at each stage while maintaining the diversity of decoding paths needed for good performance. This segmentation allows the system to avoid sorting all paths globally while still exploring multiple decoding possibilities.
Solution Approach 2:
The patent applies partial action by selecting and processing only a subset of decoding paths at each stage rather than processing all possible paths. By using a limited path width L and selectively extending only the most promising paths based on partial metrics, the algorithm achieves good decoding performance without the exhaustive calculation complexity of evaluating all 2^N paths.
2Reliability
If all paths are calculated and sorted for each decoded bit in the SCL algorithm, then decoding performance is improved, but the quantity of paths to be calculated and sorted increases
Solution Approach 1:
The patent extracts and processes only the most relevant decoding paths at each stage by using path metrics to identify and extend the L most promising paths. By taking out only the necessary subset of paths for further processing and discarding or deferring processing of less promising paths, the algorithm reduces the quantity of paths that need to be calculated and sorted while maintaining decoding performance.
Solution Approach 2:
The patent performs preliminary evaluation of paths using path metrics before full processing. By calculating preliminary metrics for all paths and then selecting only the top L paths for extensive processing and sorting, the algorithm performs the necessary work in advance in a filtered manner, reducing the overall quantity of paths that require full calculation and sorting operations.
Data Source
AI summary
The present disclosure relates to decoding methods and devices. One example method includes receiving N LLRs corresponding to a to-be-decoded signal, where N is a code length, classifying K decoded bits into reliable bits and unreliable bits based on at least one of a prior LLR or a posterior LLR, generating M decoding paths based on the N LLRs and a preset rule, 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.