Polar Code Parity Bit Mapping by Row Weight for Lower Block Error
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Solution Overview
Problem
Polar encoding techniques face challenges in selecting sub-channels for parity bits, leading to suboptimal performance in data transmission due to the conventional methods of mapping information bits to most reliable sub-channels and parity bits to less reliable sub-channels, which can result in lower encoding efficiency.
Innovation Solution
The proposed method involves selecting sub-channels for parity bits based on weight parameters, such as minimal weight and row weights, to intersperse parity bits and information bits across the most reliable sub-channels, using techniques like look-up tables or hamming weights to determine optimal sub-channel allocation for improved encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parity bits are mapped to less reliable sub-channels using conventional methods, then encoding simplicity is maintained, but encoding efficiency and error correction performance deteriorate
Solution Approach 1:
The patent changes the selection parameter from simple reliability metrics to weight parameters (such as row weights in the generator matrix). By selecting sub-channels with minimal weight parameters for parity bits, the system achieves better encoding efficiency while maintaining a systematic selection process that doesn't excessively increase complexity.
Solution Approach 2:
The patent applies different selection criteria to different types of bits: information bits are mapped to sub-channels based on reliability, while parity bits are mapped to sub-channels based on weight parameters. This local differentiation optimizes the overall encoding performance by treating different bit types according to their specific requirements.
2Reliability
If parity bits are placed in sub-channels selected by minimal weight criteria, then error detection and correction capabilities are improved, but the complexity of sub-channel allocation increases
Solution Approach 1:
The patent performs preliminary calculation of weight parameters for all sub-channels before the actual bit mapping process. By pre-computing these weight parameters and identifying which sub-channels have minimal weights, the system simplifies the subsequent allocation process while ensuring optimal error correction capabilities.
Solution Approach 2:
The patent uses the structure of the generator matrix (Kronecker product form) to systematically determine weight parameters for sub-channels. This structured approach leverages the repetitive pattern in polar code construction to efficiently identify suitable sub-channels for parity bits without exhaustive search.
3Reliability
If conventional mapping methods are used, then implementation simplicity is maintained, but block error rates increase
Solution Approach 1:
The patent changes the mapping criterion from simple reliability-based selection to weight-parameter-based selection. By using the row weights from the generator matrix as the selection parameter, the system achieves lower block error rates while maintaining a clear and systematic implementation approach that builds on standard polar code structure.
Data Source
AI summary
Embodiment techniques map parity bits to sub-channels based on their row weights. In one example, an embodiment technique includes polar encoding, with an encoder of the device, information bits and at least one parity bit using the polar code to obtain encoded data, and transmitting the encoded data to another device. The polar code comprises a plurality of sub-channels. The at least one parity bit being placed in at least one of the plurality of sub-channels. The at least one sub-channel is selected from the plurality of sub-channels based on a weight parameter.