Polar Code List Decoding With Sectioned Array Reorganization
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Solution Overview
Problem
Polar codes in wireless communication systems face computational complexity and increased latency due to complex data structure reorganization procedures during decoding, which can lead to prolonged run-times as the number of elements in the array increases.
Innovation Solution
The method involves partitioning an array of candidate path elements into sections, calculating permutation patterns for each section, and reorganizing the array section by section based on these patterns to reduce latency during the decoding operation of polar codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data structure reorganization procedures are performed during decoding of polar codes, then decoding accuracy is improved, but computational complexity and latency increase
Solution Approach 1:
The array of candidate path elements is divided into multiple sections, where each section can be processed independently. This segmentation allows the reorganization operation to be performed on smaller data subsets rather than the entire array, reducing the computational complexity from O(N^2) to O(N) while still achieving the necessary alignment for accurate decoding.
2Reliability
If data structure reorganization procedures are performed during decoding of polar codes, then decoding accuracy is improved, but decoding latency increases
Solution Approach 1:
By dividing the array into sections and processing each section independently with its own permutation pattern, the reorganization operation completes faster than processing the entire array at once. This reduces the time penalty associated with reorganization while maintaining decoding accuracy.
Solution Approach 2:
Permutation patterns for each section are pre-calculated based on the section index and other parameters before the actual decoding process. This preliminary preparation eliminates the need for complex real-time calculations during decoding, thereby reducing latency.
3Productivity
If array size increases to handle more candidate paths, then decoding performance is improved, but run-time increases proportionally to the square of the number of elements
Solution Approach 1:
The array is divided into multiple sections, each processed with its own permutation pattern. This allows the system to handle larger arrays by processing them in manageable chunks, reducing the run-time from O(N^2) to O(N) while still supporting increased array sizes for better decoding performance.
Data Source
AI summary
Techniques are described to address run-time issues and other considerations of data structure reorganization operations executed while decoding a polar code. A receiving entity (e.g., a user equipment or a base station) partitions an array, or other data structure, into sections. The array is used during a list decoding operation of a polar code. As the array is populated with path elements for candidate paths, each section is organized and a permutation pattern is calculated for each section. Upon identifying a section reorganization event, the array or subsections of the array are reorganized according the permutation patterns determined for each section.