Reliability-Ordered Bit-Flip Decoding With Quantized Soft Information
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Solution Overview
Problem
Existing decoding techniques, such as GRAND and its variants, require significant soft information and channel estimation, which can complicate implementation and increase computational complexity, especially when dealing with noisy transmission channels.
Innovation Solution
A method that uses code-book independent quantization of soft information to rank order the reliability of demodulated symbols, allowing for efficient generation or storage of noise patterns based on reliability, reducing the need for additional channel estimation and simplifying computation by starting with the most likely noise patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full soft-information is used to dynamically maintain a sequence of putative noise patterns (SGRAND), then decoding accuracy is improved, but device complexity and memory requirements increase
Solution Approach 1:
The patent segments the soft information into discrete reliability levels (e.g., high, medium, low reliability) rather than using continuous soft information values. This segmentation allows the decoder to group symbols by reliability and process them in batches, significantly reducing memory requirements while maintaining most of the decoding accuracy benefit.
Solution Approach 2:
The patent changes the parameter representation from continuous soft information (requiring high precision memory) to discrete reliability levels (requiring minimal memory). By quantizing the soft information into a small number of reliability categories, the system achieves similar decoding performance with dramatically reduced device complexity.
2Reliability
If real-number soft information is passed for each demodulated symbol (SGRAND), then decoding accuracy is improved, but information transmission requirements and computational complexity increase
Solution Approach 1:
The patent transforms the information representation from real-number soft values (high precision) to discrete reliability indicators (low precision). This parameter change reduces the information transmission requirements from continuous values to simple categorical indicators, making the system more practical for implementation while preserving decoding accuracy.
3Reliability
If sequential noise pattern subtraction is performed from most likely to least likely (GRAND), then decoding accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary grouping of symbols by reliability level before the actual decoding process. By pre-organizing symbols into reliability-based groups and determining the processing order in advance, the system avoids the time-consuming sequential processing of individual symbols while maintaining the accuracy benefits of likelihood-based ordering.
Solution Approach 2:
The patent segments the decoding process into parallel stages based on reliability groups. Instead of processing symbols one-by-one in sequential order, the system processes groups of symbols with similar reliability levels in parallel, significantly reducing overall processing time while maintaining decoding accuracy.
Data Source
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AI summary
The present application concerns an iterative bit-flipping decoding method using symbol or bit reliabilities, which is a variation of GRAND decoding and is denoted by ordered reliability bits GRAND (ORBGRAND). It comprises receiving a plurality of demodulated symbols from a noisy transmission channel; and receiving for the plurality of demodulated symbols, information indicating a ranked order of reliability of at least the most unreliable information contained within the plurality of demodulated symbols. A sequence of putative noise patterns from a most likely pattern of noise affecting the plurality of symbols through one or more successively less likely noise patterns is provided. Responsive to the information contained within the plurality of symbols not corresponding with an element of a code-book comprising a set of valid codewords, a first in the sequence of putative noise patterns is used to invert the most unreliable information of the information contained within the plurality of symbols to obtain a potential codeword, and responsive to the potential codeword not corresponding with an element of the code¬ book, repeatedly: a next likely noise pattern from the sequence of putative noise patterns is applied to invert a noise effect on the received plurality of demodulated symbols to provide a potential codeword, each successive noise pattern indicating an inversion of information for one or more demodulated symbols for a next more reliable combination of information contained within the plurality of symbols, until the potential codeword corresponds with an element of the code-book.