Polar Code Bit-Channel Simulation for Higher-Order Modulation
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Solution Overview
Problem
Extending polar code designs to systems using higher-order modulation techniques is challenging due to differing effective channels for output bits, making standard techniques inapplicable.
Innovation Solution
Simulating transmission of multiple symbols over communication channels associated with polar codes, identifying error rates of equivalent bit channels, and selecting frozen bits based on these rates to design polar codes for both binary and higher-order modulation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard polar code design techniques are used, then binary modulation systems achieve capacity efficiently, but higher-order modulation systems cannot be supported due to differing effective channels for output bits
Solution Approach 1:
The patent changes the design parameters by simulating transmission of multiple symbols representing multiple bits over communication channels, identifying error rates of equivalent bit channels, and selecting frozen bits based on these identified error rates. This parameter-based approach enables polar codes to be adapted to higher-order modulation techniques while maintaining performance optimality.
2Adaptability or versatility
If polar codes are extended to higher-order modulation techniques, then system versatility improves, but design complexity increases due to differing effective channels for output bits
Solution Approach 1:
The patent segments the design process into distinct steps: simulating transmission of multiple symbols, identifying error rates for each equivalent bit channel, and selecting frozen bits based on these identified error rates. This segmentation simplifies the overall design complexity by breaking down the complex problem of extending polar codes to higher-order modulation into manageable, systematic steps.
3Measurement precision
If frozen bits are selected based on simulated error rates, then code design accuracy improves, but computational requirements increase due to simulation overhead
Solution Approach 1:
The patent performs preliminary simulation of transmission and identification of error rates before final code design and deployment. By conducting these simulations in advance to identify the optimal frozen bits based on simulated error rates, the system achieves high measurement precision while the computational energy is expended during the design phase rather than during actual communication operations.
Data Source
AI summary
A method includes simulating transmission of multiple symbols representing multiple bits over at least one communication channel, where the multiple symbols are associated with a polar code. The method also includes identifying error rates of equivalent bit channels associated with the simulated transmission of the symbols. The method further includes selecting a specified number of the bits as frozen bits in the polar code using the identified error rates. Simulating the transmission of the symbols could include computing log likelihood ratio (LLR) values associated with the equivalent bit channels and simulating polar decoding of received symbols using the LLR values. Identifying the error rates could include calculating means and variances of the LLR values associated with the equivalent bit channels and identifying probability density functions of the LLR values using the means and variances. The selected bits could represent the specified number of bits identified as having worst error rates.