Polar Decoder Error Detection Using Frozen Bit Uncertainty
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Solution Overview
Problem
Polar codes, despite being capacity-achieving under low-complexity successive cancellation decoding, exhibit subpar finite-length performance compared to LDPC and Turbo codes, necessitating improved error detection mechanisms for competitive performance in wireless communication systems like 5G.
Innovation Solution
A method for decoding polar coded signals involves determining channel reliabilities, calculating likelihood values, and updating accumulated uncertainty to detect decoding errors, allowing for the discarding of candidate decoding paths and potential switching to more powerful decoding algorithms, thereby reducing the need for additional CRC bits and enhancing error detection within the polar decoder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional CRC coding is concatenated with polar codes, then error detection capability is improved, but system complexity and overhead increase
Solution Approach 1:
The polar decoder performs self-error detection by monitoring its own internal state (accumulated uncertainty) and comparing decoded frozen bits against known values, eliminating the need for external CRC coding and achieving error detection through the decoder's inherent structure
Solution Approach 2:
The error detection function is extracted from the separate CRC coding layer and integrated into the polar decoding process itself, where the decoder simultaneously performs decoding and error detection by tracking uncertainty accumulation and frozen bit consistency
2Reliability
If more CRC bits are added to improve error detection, then reliability is improved, but transmission overhead increases
Solution Approach 1:
The system uses the existing polar code structure and decoder state to perform error detection without requiring additional redundant bits, as the accumulated uncertainty metric and frozen bit verification provide inherent error detection capability at no extra transmission cost
3Device complexity
If polar codes use low-complexity successive cancellation decoding, then device complexity is reduced, but finite-length performance deteriorates
Solution Approach 1:
The decoder uses feedback from its own decoding process (accumulated uncertainty values and frozen bit comparison results) to detect errors and trigger corrective actions such as switching to more powerful decoding algorithms like SCL or ML decoding when errors are detected
Solution Approach 2:
The decoding system dynamically adapts its complexity by starting with low-complexity SC decoding for normal operation and switching to higher-complexity SCL or ML decoding only when error detection mechanisms indicate decoding failures, optimizing the trade-off between complexity and performance
Data Source
AI summary
A method of decoding a polar coded signal includes determining channel reliabilities for a plurality of polar coded bit channels in a data communication system including a plurality of frozen bit channels and non-frozen bit channels, selecting a frozen bit channel, calculating a likelihood value for a bit estimate associated with the frozen bit channel, generating a hard decision value for the bit estimate in response to the likelihood value, comparing the hard decision value for the bit estimate to a known value of a frozen bit transmitted on the frozen bit channel, in response to determining that the hard decision value for the bit estimate differs from the known value of the frozen bit transmitted on the frozen bit channel, updating an accumulated uncertainty, comparing the accumulated uncertainty to a threshold, and determining that a decoding error has occurred in response to the comparison.