Polar Code Decoding via Subcode Segmentation for Lower ML Complexity
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Solution Overview
Problem
The high complexity of maximum likelihood (ML) decoding for Polar codes leads to excessive computational requirements, limiting their practical application in data transmission systems.
Innovation Solution
A Polar code decoding method that divides the code into subcodes and uses independent processing modules to calculate squared Euclidean distances, followed by combined processing to reduce decoding complexity and improve throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If maximum likelihood (ML) decoding is used for Polar codes, then decoding accuracy is improved, but decoding complexity becomes excessively high
Solution Approach 1:
The Polar code of length N is divided into m subcodes of length N/m, where both N and m are integer powers of 2. Each subcode is decoded independently by separate processing modules, reducing the overall decoding complexity from O(2^N) to O(m×2^(N/m)), while maintaining ML decoding accuracy through subsequent combined processing
2Measurement precision
If maximum likelihood (ML) decoding is used for Polar codes, then decoding accuracy is improved, but decoding delay increases
Solution Approach 1:
The decoding process is segmented into independent parallel processing modules that simultaneously decode different subcodes. This parallelization reduces decoding delay by eliminating sequential processing bottlenecks, while the combined processing module ensures ML decoding accuracy is maintained through coordinated integration of subcode results
Solution Approach 2:
The decoding architecture dynamically processes multiple subcodes in parallel through independent processing modules, adapting the processing flow to reduce delays. The combined processing module dynamically integrates results from different subcodes, maintaining accuracy while optimizing the timing and sequence of operations
3Measurement precision
If maximum likelihood (ML) decoding is used for Polar codes, then decoding accuracy is improved, but throughput rate decreases
Solution Approach 1:
The Polar code decoding is segmented into m independent subcode processing modules that operate in parallel. This segmentation increases throughput rate by utilizing multiple processing units simultaneously, while the combined processing module ensures that ML decoding accuracy is preserved through coordinated integration of all subcode results
Solution Approach 2:
Multiple independent subcode processing modules are merged through a combined processing module that integrates their results. This merging approach maintains the throughput benefits of parallel processing while preserving the decoding accuracy of ML by coordinating the combined results according to the relationships between subcodes and the original Polar code
Data Source
AI summary
A method for decoding Polar codes includes: receiving a Polar code having a length of N, and dividing the Polar code into m subcodes that are coupled to each other, each subcode has a length of N/m, and each of N and m is an integer powers of 2; calculating squared Euclidean distances of input bits in the m subcodes, to obtain minimum squared Euclidean distances of the input bits that are independent of each other; obtaining, accordingly a minimum squared Euclidean distance of input bits that are coupled to each other in the m subcodes; and obtaining input bits that are in the m subcodes and that meet the independent minimum squared Euclidean distances and the combined minimum squared Euclidean distance, and obtaining a decoding result of the Polar code with reference to relationships between the m subcodes and the Polar code.