Polar Code Bit Allocation for Non-Power-of-2 Rate Matching
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Solution Overview
Problem
Current polar code encoding schemes struggle with rate matching when the code length is not an integer power of 2, lacking a quick and flexible method for information bit construction and rate matching.
Innovation Solution
A data processing method that allows for quick information bit construction and rate matching by using an allocation sequence to determine the quantity of information bits in each sub-block, enabling encoding and decoding even when the code length is not an integer multiple of 2n.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Gaussian approximation is used for encoding matrix construction, then encoding can be performed for non-integer power of 2 code lengths, but the method lacks flexibility and quick construction capability
Solution Approach 1:
The encoding process is segmented into multiple sub-blocks, each of which can be independently constructed using the allocation sequence. This allows the overall encoding to be performed quickly through parallel processing of sub-blocks while maintaining adaptability to various code lengths by adjusting the number and size of sub-blocks.
Solution Approach 2:
The allocation sequence is pre-calculated and stored, containing the optimal distribution of information bits across sub-blocks for different code lengths and code rates. This preliminary action eliminates the need for complex real-time calculations during encoding, significantly improving encoding speed while maintaining adaptability.
2Reliability
If sub-block interleaving rate matching is used, then rate matching can be implemented for ordinary polar codes, but the method is only applicable to encoding matrices with mother code length of 2n
Solution Approach 1:
The allocation sequence method serves as a universal rate matching approach that works for both integer power of 2 and non-integer power of 2 code lengths. By defining a general framework that accommodates various code lengths through flexible sub-block partitioning, it eliminates the limitation of traditional methods that only work for specific code lengths.
Solution Approach 2:
The method changes the parameter representation from fixed mother code length 2n to variable code lengths determined by the allocation sequence. The allocation sequence parameters (A0, A1, ..., AS-2) can be adjusted to match different code lengths and code rates, providing universal applicability while maintaining optimal rate matching performance.
3Reliability
If code length matching process with puncturing and retransmission is used, then rate matching can be implemented, but the process increases system complexity and power consumption
Solution Approach 1:
The method extracts the rate matching function from the complex process of puncturing and retransmission into a simple allocation sequence lookup and sub-block division operation. By separating the rate matching logic into a pre-calculated allocation sequence, the actual encoding process becomes much simpler and requires less computational resources.
Solution Approach 2:
Instead of performing complex puncturing and retransmission operations, the method creates a simplified copy of the encoding process that directly applies the allocation sequence to distribute information bits. This copying approach maintains the essential rate matching functionality while eliminating the need for iterative puncturing and retransmission, reducing complexity and power consumption.
Data Source
AI summary
A data processing method, an apparatus, and a device, so that an information bit construction and encoding method based on an allocation sequence is designed. According to the method, a quantity of information bits in each to-be-encoded sub-block in a to-be-encoded block may be determined based on a quantity of information bits and an allocation sequence, to implement information bit construction and encoding.