Polar Code SCL Decoding with Reduced Path Sorting Complexity
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Solution Overview
Problem
Current polar code decoding methods using the successive cancellation list (SCL) algorithm are inefficient due to high complexity and slow decoding speed, particularly in parallel decoding processes where numerous decoding paths need to be sorted and evaluated.
Innovation Solution
The proposed method reduces decoding complexity by determining a second decoding path based on a limited number of first decoding paths, where the quantity of second decoding paths corresponding to each first decoding path is less than 2n, thereby reducing sorting complexity and improving efficiency in SCL parallel decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional SCL parallel decoding is used to ensure decoding accuracy, then decoding reliability is improved, but decoding complexity increases and decoding speed decreases
Solution Approach 1:
The patent segments the decoding process into multiple groups of bits, where each group is decoded separately using L1 first decoding paths. This segmentation reduces the overall complexity by breaking down the large-scale sorting problem into smaller, manageable sub-problems that can be processed in parallel.
Solution Approach 2:
Instead of evaluating all possible 2^n decoding paths for each group, the patent uses a limited number L1 of first decoding paths (where L1 < 2^n). This partial action approach maintains sufficient decoding accuracy while significantly reducing computational complexity and sorting requirements.
2Reliability
If traditional SCL parallel decoding is used to maintain decoding quality, then decoding reliability is improved, but decoding speed decreases
Solution Approach 1:
By dividing the decoding into multiple bit groups processed in parallel, the patent enables simultaneous execution of multiple decoding operations. This segmentation directly improves decoding speed by utilizing parallel processing capabilities while maintaining accuracy through the use of L1 decoding paths for each group.
Solution Approach 2:
The patent processes only L1 first decoding paths per group instead of all possible paths, reducing the computational burden per group. When combined with parallel processing of multiple groups, this partial action approach achieves both high speed and high accuracy.
3Measurement precision
If all possible decoding paths are evaluated to ensure accurate results, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent evaluates only L1 first decoding paths for each group of bits, where L1 is much smaller than the total number of possible paths 2^n. This partial evaluation maintains sufficient decoding precision while dramatically reducing sorting complexity and computational requirements.
Solution Approach 2:
The decoding space is segmented into multiple groups, each processed with L1 paths. This segmentation allows the system to manage complexity by handling smaller subsets of paths independently, reducing the overall sorting burden compared to evaluating all paths globally.
Data Source
AI summary
One example method includes obtaining L1 first decoding paths of an (i−1)th group of to-be-decoded bits, where i is an integer, received data corresponds to P groups of to-be-decoded bits, and 1<i≤P, determining at least one second decoding path corresponding to each first decoding path, where a quantity of second decoding paths corresponding to each first decoding path is less than 2n, and where n is a quantity of information bits included in an ith group of to-be-decoded bits, and determining at least one reserved decoding path of the ith group of to-be-decoded bits in second decoding paths corresponding to the L1 first decoding paths. The at least one reserved decoding path includes a decoding result of the ith group of to-be-decoded bits.