Polar Code Parity Mapping Using Sub-Channel Row Weights
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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 inefficient distribution of information and parity bits across channels.
Innovation Solution
The method involves selecting sub-channels for parity bits based on weight parameters, such as minimal weight and row weights, to optimize their placement across the most reliable sub-channels, thereby improving encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If parity bits are placed in sub-channels without considering weight parameters, then the encoding process is simpler, but the transmission reliability deteriorates
Solution Approach 1:
The patent applies parameter changes by considering weight parameters (such as row weights in the generator matrix) when selecting sub-channels for parity bits. Instead of using a fixed or simple selection method, the encoder dynamically evaluates weight parameters to determine the optimal placement of parity bits in sub-channels with appropriate reliability characteristics, thereby improving transmission reliability while managing encoding complexity through systematic parameter-based selection.
2Reliability
If parity bits are distributed across more sub-channels, then decoding reliability improves, but the encoding time increases
Solution Approach 1:
The patent applies local quality by selectively placing parity bits in specific sub-channels based on their individual weight parameters and reliability characteristics rather than uniformly distributing them. This allows the system to concentrate parity protection in sub-channels that provide the most benefit for decoding reliability, while avoiding unnecessary processing in sub-channels where additional parity bits would not significantly improve performance, thus reducing overall encoding time.
3Productivity
If sub-channels are selected based on minimal weight criteria, then encoding efficiency improves, but the distribution of parity bits becomes less uniform
Solution Approach 1:
The patent resolves this contradiction by using weight parameters as a systematic criterion for selecting sub-channels for parity bits. By evaluating the row weights in the generator matrix, the encoder can efficiently identify suitable sub-channels while maintaining a controlled distribution pattern. This parameter-based approach ensures that parity bits are placed in sub-channels that optimize encoding efficiency while preventing excessive concentration in a single sub-channel, thus maintaining reasonable distribution uniformity.
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.