Polar Code Parity Bit Placement Using Row-Weight Sub-Channels
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Solution Overview
Problem
Polar encoding techniques face inefficiencies in selecting sub-channels for parity bits, leading to suboptimal performance in error detection and correction, as existing methods often map information bits to the most reliable sub-channels and parity bits to less reliable ones, resulting in lower encoding performance.
Innovation Solution
The proposed method involves selecting sub-channels for parity bits based on weight parameters, such as minimal row-weight or hamming weight, to intersperse them among information bits, improving decoding probability and overall encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If parity bits are mapped to less reliable sub-channels, then encoding complexity is reduced, but error detection and correction performance deteriorates
Solution Approach 1:
The patent inverts the conventional mapping approach by placing parity bits on more reliable sub-channels instead of less reliable ones. This counterintuitive strategy improves error detection and correction performance because parity bits positioned on reliable sub-channels can more effectively detect and correct errors in information bits located on less reliable sub-channels, thereby resolving the performance deterioration issue while maintaining encoding simplicity
Solution Approach 2:
The patent changes the selection criterion for parity bit positioning from reliability-based to weight-parameter-based selection. By using weight parameters (such as row-weight or hamming weight) to identify and select sub-channels for parity bits, the system achieves optimal error correction performance without increasing encoding complexity, as the weight parameter calculation is computationally efficient
2Measurement precision
If information bits are mapped to the most reliable sub-channels, then decoding accuracy is improved, but encoding performance becomes suboptimal when parity bits are placed on less reliable sub-channels
Solution Approach 1:
The patent applies local quality by differentiating the selection criteria for different bit types. Information bits are mapped to sub-channels selected by reliability metrics, while parity bits are mapped to sub-channels selected by weight parameters. This localized optimization allows each bit type to occupy the most suitable sub-channels for its function, thereby simultaneously achieving high decoding accuracy and optimal encoding performance
Solution Approach 2:
The patent introduces dynamic selection of sub-channels for parity bits based on weight parameters. Rather than using a fixed mapping rule, the system dynamically identifies suitable sub-channels by calculating weight parameters and selecting those that meet the selection criterion. This dynamic approach enables the system to adapt to different channel conditions and code configurations, improving overall encoding performance while maintaining decoding accuracy
Data Source
AI summary
Embodiment techniques map parity bits to sub-channels based on their row weights. The row weight for a sub-channel may be viewed as the number of “ones” in the corresponding row of the Kronecker matrix or as a power of 2 with the exponent (i.e. the hamming weight) being the number of “ones” in the binary representation of the sub-channel index (further described below). In one embodiment, candidate sub-channels that have certain row weight values are reserved for parity bit(s). Thereafter, K information bits may be mapped to the K most reliable remaining sub-channels, and a number of frozen bits (e.g. N−K) may be mapped to the least reliable remaining sub-channels. Parity bits may then mapped to the candidate sub-channels, and parity bit values are determined based on a function of the information bits.