Punctured Polar Code Decoding with Polarization Weight Ranking
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently decoding punctured polar codes due to the uncertainty in bit locations and the lack of effective methods for determining the reliability of bit channels, particularly in scenarios where bits are punctured, leading to reduced decoding accuracy.
Innovation Solution
A method is introduced to calculate polarization weights for punctured polar codes, allowing for the ranking of bit locations based on their reliability, which are then scaled according to the number of repetition operations affected by puncturing, thereby improving decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If puncturing is applied to achieve a given code rate, then transmission efficiency is improved, but decoding accuracy deteriorates due to uncertainty in bit locations
Solution Approach 1:
The patent applies preliminary action by pre-calculating polarization weights for each bit channel before decoding. These weights are computed based on the puncturing pattern and used to rank bit locations in advance, allowing the decoder to prioritize reliable bits during the decoding process. This pre-computation enables the system to maintain high decoding accuracy despite the uncertainty introduced by puncturing.
Solution Approach 2:
The patent changes the parameter of bit channel reliability by introducing polarization weights that quantify the reliability of each bit location. By scaling these weights according to the number of repetition operations affected by puncturing, the system transforms the abstract concept of reliability into a concrete, measurable parameter that can be used for optimal bit location identification and decoding.
2Device complexity
If traditional polar code decoding is used without considering puncturing, then device complexity is reduced, but information bit location identification accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-calculating polarization weights for each bit channel before decoding. These weights are computed based on the puncturing pattern and used to rank bit locations in advance, allowing the decoder to prioritize reliable bits during the decoding process. This pre-computation enables the system to maintain high decoding accuracy despite the uncertainty introduced by puncturing.
Solution Approach 2:
The patent changes the parameter of bit channel reliability by introducing polarization weights that quantify the reliability of each bit location. By scaling these weights according to the number of repetition operations affected by puncturing, the system transforms the abstract concept of reliability into a concrete, measurable parameter that can be used for optimal bit location identification and decoding.
Data Source
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AI summary
Techniques are described for wireless communication. One method includes identifying a set of punctured bit locations in a received codeword. The received codeword is encoded using a polar code. The method also includes identifying a set of information bit locations of the polar code, with the set of information bit locations being determined based at least in part on polarization weights per polarized bit-channel of a polar code decoder that are a function of nulled repetition operations per polarization stage of the polar code identified based at least in part on the set of punctured bit locations. The method further includes processing the received codeword using the polar code decoder to obtain an information bit vector at the set of information bit locations.