Polar Code Soft-Output Decoding with SCL Candidate Likelihoods
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Solution Overview
Problem
Current polar code decoders, such as SC and SCL, primarily provide hard-decision outputs, limiting their ability to efficiently correct errors in shorter code lengths and requiring additional iterations for soft-decision information, which is computationally expensive.
Innovation Solution
Modifying the list-decoding of the SCL decoder to produce soft-outputs by comparing candidate codewords against the noisy input, calculating likelihoods based on distance differences, and optionally using CRC codes to validate decoding paths, allowing for efficient soft-output generation without separate iterations for each bit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SC or SCL decoders are used to provide hard-decision outputs, then decoding speed is maintained, but error correction performance for shorter code lengths is limited
Solution Approach 1:
The patent segments the decoding process into two distinct phases: (1) SCL decoding phase that generates candidate codewords and maintains decoding speed, and (2) soft-output generation phase that computes likelihoods only for the final candidate codewords. This segmentation allows the system to maintain fast hard-decision decoding while adding efficient soft-output capability for improved error correction.
Solution Approach 2:
The patent applies partial action by computing soft-outputs only for the final candidate codewords returned by SCL decoding, rather than performing full iterative soft-decoding for all possible codewords. This partial computation approach significantly reduces computational complexity while still providing the error correction benefits of soft-decision decoding.
2Measurement precision
If multiple iterations are performed to generate soft-decision information, then soft-output accuracy is improved, but computational complexity and time increase
Solution Approach 1:
The patent performs preliminary SCL decoding to identify the most likely candidate codewords before generating soft-outputs. By pre-selecting only the final candidate codewords from SCL decoding as the basis for soft-output computation, the system avoids the need for multiple iterative decoding passes, thereby reducing computational complexity while maintaining soft-output accuracy.
3Measurement precision
If separate iterations are performed for each bit to generate soft-outputs, then bit-level accuracy is improved, but decoding time increases
Solution Approach 1:
The patent merges the soft-output generation process with the final SCL decoding result by computing likelihoods for all bits simultaneously based on the distance between the received signal and the final candidate codeword. This unified approach eliminates the need for separate iterative processing for each bit, reducing decoding time while maintaining bit-level accuracy through the distance-based likelihood computation.
Data Source
AI summary
A receiver includes a polar decoder for decoding an encoded codeword transmitted over a communication channel. The receiver includes a front end to receive over a communication channel a codeword including a sequence of bits modified with noise of the communication channel and a soft decoder operated by a processor to produce a soft output of the decoding. The codeword is encoded by at least one polar encoder with a polar code. The processor is configured to estimate possible values of the bits of the received codeword using a successive cancelation list (SCL) decoding to produce a set of candidate codewords, determine a distance between each candidate codeword and a soft input to the soft decoder, and determine a likelihood of a value of a bit in the sequence of bits using a difference of distances of the candidate codewords closest to the received codeword and having opposite values at the position of the bit.