Polar Code Decoding with Bit Reordering for Frozen-Bit Awareness
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Solution Overview
Problem
Conventional Successive Cancellation (SC) and SC List (SCL) polar decoders perform suboptimally due to their successive nature, ignoring future frozen bits during decoding, which affects performance, and are not competitive with Maximum-Likelihood (ML) decoders in finite-length performance.
Innovation Solution
Applying self-similar transformations, such as bit-reversal or last-to-first permutations, to the coded bits before decoding allows conventional SC or SCL decoders to account for future frozen bits, enhancing performance without increasing decoding complexity by reordering the decoding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional Successive Cancellation (SC) or SC List (SCL) decoders are used, then decoding complexity is kept low, but decoding performance is suboptimal because future frozen bits are ignored during the successive decoding process
Solution Approach 1:
The patent applies preliminary action by performing bit-reversal permutation on the received coded bits before decoding. This reordering operation is executed in advance to transform the decoding order, allowing future frozen bits to be properly accounted for during the successive cancellation process. The transformation prepares the data in a specific order that enables conventional decoders to achieve better performance without modifying the decoder structure itself.
2Reliability
If the decoding order is changed to account for future frozen bits, then decoding performance improves, but the complexity of the decoding process increases
Solution Approach 1:
The patent applies inversion by using bit-reversal permutation, which reverses the natural order of bit processing. Instead of decoding bits in their original sequential order, the transformation decodes them in reverse order (from last to first). This inverted approach allows the decoder to naturally incorporate knowledge of future frozen bits that would otherwise be unavailable in the conventional forward decoding process, thereby improving performance without requiring complex adaptive decoding logic.
Data Source
AI summary
Systems and methods for performing polar decoding using a transformation of the coded bits prior to polar decoding and an inverse transformation of the resulting data bits after polar decoding are disclosed. In some embodiments, a method of operation of a receiving node to perform polar decoding comprises transforming a received code bit vector yN in accordance with a transformation TN to thereby provide a transformed code bit vector Y′N=YNTN, performing polar decoding of the transformed code bit vector y′N to thereby provide a transformed data bit vector v′N, and inversely transforming the transformed data bit vector v′N in accordance with an inverse transformation TN−1 to thereby provide an estimated data bit vector vN=v′NTN−1. In some embodiments, the transformation TN re-orders coded bits in the coded bit vector yN such that some future frozen bit(s) are swapped in front of some information bit(s) prior to performing polar decoding.